Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations49866
Missing cells274607
Missing cells (%)23.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 MiB
Average record size in memory177.0 B

Variable types

Text14
Numeric6
Categorical2
DateTime1

Alerts

final_budget is highly overall correlated with final_domestic_boxoffice and 1 other fieldsHigh correlation
final_domestic_boxoffice is highly overall correlated with final_budget and 1 other fieldsHigh correlation
final_worldwide_boxoffice is highly overall correlated with final_budget and 1 other fieldsHigh correlation
_merge is highly imbalanced (57.0%) Imbalance
final_budget has 36038 (72.3%) missing values Missing
final_worldwide_boxoffice has 37177 (74.6%) missing values Missing
final_domestic_boxoffice has 34819 (69.8%) missing values Missing
final_genres has 3706 (7.4%) missing values Missing
final_runtime has 5534 (11.1%) missing values Missing
final_overview has 5196 (10.4%) missing values Missing
final_certificate has 27216 (54.6%) missing values Missing
final_original_language has 4589 (9.2%) missing values Missing
final_rating has 4578 (9.2%) missing values Missing
final_production_countries has 4578 (9.2%) missing values Missing
imdb_id has 4579 (9.2%) missing values Missing
production_companies has 4578 (9.2%) missing values Missing
cast has 4578 (9.2%) missing values Missing
crew has 4578 (9.2%) missing values Missing
director has 23137 (46.4%) missing values Missing
director_id has 23137 (46.4%) missing values Missing
star has 23147 (46.4%) missing values Missing
star_id has 23144 (46.4%) missing values Missing
final_domestic_boxoffice has 804 (1.6%) zeros Zeros
final_rating has 2936 (5.9%) zeros Zeros

Reproduction

Analysis started2025-03-26 21:47:56.704687
Analysis finished2025-03-26 21:49:24.336762
Duration1 minute and 27.63 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Distinct44940
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:25.808752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length109
Median length84
Mean length16.260819
Min length1

Characters and Unicode

Total characters810862
Distinct characters2946
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40838 ?
Unique (%)81.9%

Sample

1st row!Women Art Revolution
2nd row#1 Cheerleader Camp
3rd row#chicagoGirl
4th row#Horror
5th row#Pellichoopulu
ValueCountFrequency (%)
the 11602
 
8.0%
of 3698
 
2.6%
a 1844
 
1.3%
in 1386
 
1.0%
and 1205
 
0.8%
la 1041
 
0.7%
918
 
0.6%
to 892
 
0.6%
de 739
 
0.5%
man 568
 
0.4%
Other values (35887) 120274
83.4%
2025-03-26T17:49:28.187980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94281
 
11.6%
e 77683
 
9.6%
a 53486
 
6.6%
o 46165
 
5.7%
i 43055
 
5.3%
n 42932
 
5.3%
r 41452
 
5.1%
t 36838
 
4.5%
s 31340
 
3.9%
l 27841
 
3.4%
Other values (2936) 315789
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 810862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
94281
 
11.6%
e 77683
 
9.6%
a 53486
 
6.6%
o 46165
 
5.7%
i 43055
 
5.3%
n 42932
 
5.3%
r 41452
 
5.1%
t 36838
 
4.5%
s 31340
 
3.9%
l 27841
 
3.4%
Other values (2936) 315789
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 810862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
94281
 
11.6%
e 77683
 
9.6%
a 53486
 
6.6%
o 46165
 
5.7%
i 43055
 
5.3%
n 42932
 
5.3%
r 41452
 
5.1%
t 36838
 
4.5%
s 31340
 
3.9%
l 27841
 
3.4%
Other values (2936) 315789
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 810862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
94281
 
11.6%
e 77683
 
9.6%
a 53486
 
6.6%
o 46165
 
5.7%
i 43055
 
5.3%
n 42932
 
5.3%
r 41452
 
5.1%
t 36838
 
4.5%
s 31340
 
3.9%
l 27841
 
3.4%
Other values (2936) 315789
38.9%

final_budget
Real number (ℝ)

High correlation  Missing 

Distinct1348
Distinct (%)9.7%
Missing36038
Missing (%)72.3%
Infinite0
Infinite (%)0.0%
Mean25340963
Minimum1
Maximum5.332 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:28.436224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100000
Q12500000
median10000000
Q330000000
95-th percentile1.05 × 108
Maximum5.332 × 108
Range5.332 × 108
Interquartile range (IQR)27500000

Descriptive statistics

Standard deviation39700100
Coefficient of variation (CV)1.5666374
Kurtosis13.674426
Mean25340963
Median Absolute Deviation (MAD)9050000
Skewness3.1351164
Sum3.5041483 × 1011
Variance1.576098 × 1015
MonotonicityNot monotonic
2025-03-26T17:49:28.582968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 454
 
0.9%
5000000 432
 
0.9%
20000000 418
 
0.8%
15000000 360
 
0.7%
2000000 353
 
0.7%
30000000 333
 
0.7%
25000000 329
 
0.7%
3000000 326
 
0.7%
1000000 305
 
0.6%
40000000 297
 
0.6%
Other values (1338) 10221
 
20.5%
(Missing) 36038
72.3%
ValueCountFrequency (%)
1 25
0.1%
2 14
< 0.1%
3 9
 
< 0.1%
4 7
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 3
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
10 10
 
< 0.1%
ValueCountFrequency (%)
533200000 1
 
< 0.1%
400000000 3
< 0.1%
380000000 1
 
< 0.1%
379000000 1
 
< 0.1%
365000000 1
 
< 0.1%
340000000 1
 
< 0.1%
330400000 1
 
< 0.1%
300000000 5
< 0.1%
290000000 1
 
< 0.1%
280200000 1
 
< 0.1%

final_worldwide_boxoffice
Real number (ℝ)

High correlation  Missing 

Distinct11446
Distinct (%)90.2%
Missing37177
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean78039564
Minimum0
Maximum2.923706 × 109
Zeros428
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:28.744628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3005.8
Q12300000
median18875011
Q374870866
95-th percentile3.5837453 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)72570866

Descriptive statistics

Standard deviation1.6774213 × 108
Coefficient of variation (CV)2.1494499
Kurtosis43.836131
Mean78039564
Median Absolute Deviation (MAD)18659672
Skewness5.2419344
Sum9.9024403 × 1011
Variance2.8137424 × 1016
MonotonicityNot monotonic
2025-03-26T17:49:28.884324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 428
 
0.9%
11000000 23
 
< 0.1%
2000000 22
 
< 0.1%
10000000 22
 
< 0.1%
8000000 20
 
< 0.1%
6000000 20
 
< 0.1%
12000000 18
 
< 0.1%
5000000 17
 
< 0.1%
7000000 14
 
< 0.1%
3000000 13
 
< 0.1%
Other values (11436) 12092
 
24.2%
(Missing) 37177
74.6%
ValueCountFrequency (%)
0 428
0.9%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2923706026 1
< 0.1%
2748242781 1
< 0.1%
2743577587 1
< 0.1%
2320250281 1
< 0.1%
2223048786 1
< 0.1%
2068223624 1
< 0.1%
2056046835 1
< 0.1%
2048359754 1
< 0.1%
1979091486 1
< 0.1%
1921206586 1
< 0.1%

final_domestic_boxoffice
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct12400
Distinct (%)82.4%
Missing34819
Missing (%)69.8%
Infinite0
Infinite (%)0.0%
Mean30254133
Minimum0
Maximum9.3666222 × 108
Zeros804
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:29.031863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1338837.5
median6650206
Q333631822
95-th percentile1.3670877 × 108
Maximum9.3666222 × 108
Range9.3666222 × 108
Interquartile range (IQR)33292984

Descriptive statistics

Standard deviation61021707
Coefficient of variation (CV)2.0169709
Kurtosis34.621107
Mean30254133
Median Absolute Deviation (MAD)6637422
Skewness4.7073205
Sum4.5523394 × 1011
Variance3.7236487 × 1015
MonotonicityNot monotonic
2025-03-26T17:49:29.164443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 804
 
1.6%
10000000 18
 
< 0.1%
7000000 15
 
< 0.1%
8000000 15
 
< 0.1%
4360000 14
 
< 0.1%
1000000 14
 
< 0.1%
5000000 14
 
< 0.1%
4000000 12
 
< 0.1%
2000000 11
 
< 0.1%
11000000 11
 
< 0.1%
Other values (12390) 14119
28.3%
(Missing) 34819
69.8%
ValueCountFrequency (%)
0 804
1.6%
30 1
 
< 0.1%
80 1
 
< 0.1%
145 1
 
< 0.1%
181 1
 
< 0.1%
199 1
 
< 0.1%
252 2
 
< 0.1%
256 1
 
< 0.1%
264 1
 
< 0.1%
309 1
 
< 0.1%
ValueCountFrequency (%)
936662225 2
< 0.1%
858373000 1
< 0.1%
814811535 1
< 0.1%
785221649 1
< 0.1%
749766139 1
< 0.1%
718732821 1
< 0.1%
700059566 1
< 0.1%
684075767 1
< 0.1%
678815482 1
< 0.1%
674460013 1
< 0.1%

final_genres
Text

Missing 

Distinct4071
Distinct (%)8.8%
Missing3706
Missing (%)7.4%
Memory size389.7 KiB
2025-03-26T17:49:29.485228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length264
Median length225
Mean length61.831282
Min length2

Characters and Unicode

Total characters2854132
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2366 ?
Unique (%)5.1%

Sample

1st row[{'id': 99, 'name': 'Documentary'}]
2nd row[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}]
3rd row[{'id': 99, 'name': 'Documentary'}]
4th row[{'id': 18, 'name': 'Drama'}, {'id': 9648, 'name': 'Mystery'}, {'id': 27, 'name': 'Horror'}, {'id': 53, 'name': 'Thriller'}]
5th row[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]
ValueCountFrequency (%)
id 90826
24.5%
name 90826
24.5%
drama 20432
 
5.5%
18 20208
 
5.5%
comedy 13329
 
3.6%
35 13163
 
3.6%
53 7601
 
2.1%
thriller 7601
 
2.1%
action 6721
 
1.8%
10749 6720
 
1.8%
Other values (40) 92962
25.1%
2025-03-26T17:49:29.933544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 544956
19.1%
324229
 
11.4%
: 181652
 
6.4%
a 153009
 
5.4%
e 147285
 
5.2%
m 144254
 
5.1%
, 138743
 
4.9%
i 130660
 
4.6%
n 126845
 
4.4%
d 107800
 
3.8%
Other values (42) 854699
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2854132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 544956
19.1%
324229
 
11.4%
: 181652
 
6.4%
a 153009
 
5.4%
e 147285
 
5.2%
m 144254
 
5.1%
, 138743
 
4.9%
i 130660
 
4.6%
n 126845
 
4.4%
d 107800
 
3.8%
Other values (42) 854699
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2854132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 544956
19.1%
324229
 
11.4%
: 181652
 
6.4%
a 153009
 
5.4%
e 147285
 
5.2%
m 144254
 
5.1%
, 138743
 
4.9%
i 130660
 
4.6%
n 126845
 
4.4%
d 107800
 
3.8%
Other values (42) 854699
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2854132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 544956
19.1%
324229
 
11.4%
: 181652
 
6.4%
a 153009
 
5.4%
e 147285
 
5.2%
m 144254
 
5.1%
, 138743
 
4.9%
i 130660
 
4.6%
n 126845
 
4.4%
d 107800
 
3.8%
Other values (42) 854699
29.9%

final_runtime
Real number (ℝ)

Missing 

Distinct353
Distinct (%)0.8%
Missing5534
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean97.730556
Minimum0
Maximum1256
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:30.080817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q187
median95
Q3108
95-th percentile139
Maximum1256
Range1256
Interquartile range (IQR)21

Descriptive statistics

Standard deviation34.455249
Coefficient of variation (CV)0.35255349
Kurtosis143.55352
Mean97.730556
Median Absolute Deviation (MAD)10
Skewness6.7045416
Sum4332591
Variance1187.1642
MonotonicityNot monotonic
2025-03-26T17:49:30.226499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2564
 
5.1%
100 1494
 
3.0%
95 1420
 
2.8%
93 1225
 
2.5%
96 1121
 
2.2%
92 1089
 
2.2%
91 1077
 
2.2%
94 1068
 
2.1%
97 1043
 
2.1%
98 1037
 
2.1%
Other values (343) 31194
62.6%
(Missing) 5534
 
11.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 107
0.2%
2 33
 
0.1%
3 48
0.1%
4 50
0.1%
5 51
0.1%
6 72
0.1%
7 103
0.2%
8 78
0.2%
9 63
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

final_overview
Text

Missing 

Distinct44479
Distinct (%)99.6%
Missing5196
Missing (%)10.4%
Memory size389.7 KiB
2025-03-26T17:49:30.743782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length787
Mean length322.12505
Min length1

Characters and Unicode

Total characters14389326
Distinct characters429
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44447 ?
Unique (%)99.5%

Sample

1st rowThrough intimate interviews, provocative art, and rare, historical film and video footage, this feature documentary reveals how art addressing political consequences of discrimination and violence, the Feminist Art Revolution radically transformed the art and culture of our times.
2nd rowA pair of horny college guys get summer jobs at a sexy cheerleader camp.
3rd rowFrom her childhood bedroom in the Chicago suburbs, an American teenage girl uses social media to run the revolution in Syria. Armed with Facebook, Twitter, Skype and cameraphones, she helps her social network in Damascus and Homs braves snipers and shelling in the streets and the world the human rights atrocities of one of the most brutal dictators. But as the revolution rages on, everyone in the network must decide what is the most effective way to fight a dictator: social media or AK-47s.
4th rowInspired by actual events, a group of 12 year old girls face a night of horror when the compulsive addiction of an online social media game turns a moment of cyber bullying into a night of insanity.
5th rowRaj Kandukuri and BiG Ben Films in association with Vinoothna Geetha presents #Pellichoopulu, a Telugu romantic comedy about two individuals Prashanth and Chitra who meet at a 'Pellichoopulu'. The story is about a modern day couple anchored down by their traditional roots. When fate plays havoc in their lives, they set off on a journey to find their dreams, face their fears and ultimately find true love. Directed by Tharun Bhascker of Anukokunda and Sainma fame, the film stars Vijay Deverakonda and Ritu Varma.
ValueCountFrequency (%)
the 138243
 
5.6%
a 99167
 
4.0%
and 75338
 
3.1%
to 73417
 
3.0%
of 69678
 
2.8%
in 48286
 
2.0%
is 36579
 
1.5%
his 36235
 
1.5%
with 23925
 
1.0%
her 21490
 
0.9%
Other values (97290) 1830422
74.6%
2025-03-26T17:49:31.422169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2411696
16.8%
e 1365824
 
9.5%
a 941961
 
6.5%
t 935949
 
6.5%
i 852797
 
5.9%
o 830980
 
5.8%
n 823680
 
5.7%
s 768967
 
5.3%
r 745341
 
5.2%
h 601526
 
4.2%
Other values (419) 4110605
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14389326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2411696
16.8%
e 1365824
 
9.5%
a 941961
 
6.5%
t 935949
 
6.5%
i 852797
 
5.9%
o 830980
 
5.8%
n 823680
 
5.7%
s 768967
 
5.3%
r 745341
 
5.2%
h 601526
 
4.2%
Other values (419) 4110605
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14389326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2411696
16.8%
e 1365824
 
9.5%
a 941961
 
6.5%
t 935949
 
6.5%
i 852797
 
5.9%
o 830980
 
5.8%
n 823680
 
5.7%
s 768967
 
5.3%
r 745341
 
5.2%
h 601526
 
4.2%
Other values (419) 4110605
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14389326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2411696
16.8%
e 1365824
 
9.5%
a 941961
 
6.5%
t 935949
 
6.5%
i 852797
 
5.9%
o 830980
 
5.8%
n 823680
 
5.7%
s 768967
 
5.3%
r 745341
 
5.2%
h 601526
 
4.2%
Other values (419) 4110605
28.6%

final_certificate
Categorical

Missing 

Distinct30
Distinct (%)0.1%
Missing27216
Missing (%)54.6%
Memory size389.7 KiB
R
7592 
Not Rated
4603 
PG-13
2922 
PG
2388 
Approved
1720 
Other values (25)
3425 

Length

Max length9
Median length8
Mean length4.357351
Min length1

Characters and Unicode

Total characters98694
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNot Rated
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 7592
 
15.2%
Not Rated 4603
 
9.2%
PG-13 2922
 
5.9%
PG 2388
 
4.8%
Approved 1720
 
3.4%
Passed 1256
 
2.5%
Unrated 693
 
1.4%
G 594
 
1.2%
TV-MA 228
 
0.5%
TV-14 208
 
0.4%
Other values (20) 446
 
0.9%
(Missing) 27216
54.6%

Length

2025-03-26T17:49:31.562682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 7592
27.9%
not 4603
16.9%
rated 4603
16.9%
pg-13 2922
 
10.7%
pg 2388
 
8.8%
approved 1720
 
6.3%
passed 1256
 
4.6%
unrated 693
 
2.5%
g 594
 
2.2%
tv-ma 228
 
0.8%
Other values (20) 654
 
2.4%

Most occurring characters

ValueCountFrequency (%)
R 12195
12.4%
t 9899
 
10.0%
e 8275
 
8.4%
d 8272
 
8.4%
P 6767
 
6.9%
a 6552
 
6.6%
o 6323
 
6.4%
G 6140
 
6.2%
N 4629
 
4.7%
4603
 
4.7%
Other values (24) 25039
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 12195
12.4%
t 9899
 
10.0%
e 8275
 
8.4%
d 8272
 
8.4%
P 6767
 
6.9%
a 6552
 
6.6%
o 6323
 
6.4%
G 6140
 
6.2%
N 4629
 
4.7%
4603
 
4.7%
Other values (24) 25039
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 12195
12.4%
t 9899
 
10.0%
e 8275
 
8.4%
d 8272
 
8.4%
P 6767
 
6.9%
a 6552
 
6.6%
o 6323
 
6.4%
G 6140
 
6.2%
N 4629
 
4.7%
4603
 
4.7%
Other values (24) 25039
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 12195
12.4%
t 9899
 
10.0%
e 8275
 
8.4%
d 8272
 
8.4%
P 6767
 
6.9%
a 6552
 
6.6%
o 6323
 
6.4%
G 6140
 
6.2%
N 4629
 
4.7%
4603
 
4.7%
Other values (24) 25039
25.4%
Distinct89
Distinct (%)0.2%
Missing4589
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:31.782425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters90554
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowde
5th rowte
ValueCountFrequency (%)
en 32138
71.0%
fr 2432
 
5.4%
it 1528
 
3.4%
ja 1345
 
3.0%
de 1076
 
2.4%
es 990
 
2.2%
ru 822
 
1.8%
hi 507
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (79) 3587
 
7.9%
2025-03-26T17:49:32.114346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34458
38.1%
n 32846
36.3%
r 3625
 
4.0%
f 2827
 
3.1%
i 2385
 
2.6%
t 2249
 
2.5%
a 1833
 
2.0%
s 1648
 
1.8%
j 1346
 
1.5%
d 1320
 
1.5%
Other values (16) 6017
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34458
38.1%
n 32846
36.3%
r 3625
 
4.0%
f 2827
 
3.1%
i 2385
 
2.6%
t 2249
 
2.5%
a 1833
 
2.0%
s 1648
 
1.8%
j 1346
 
1.5%
d 1320
 
1.5%
Other values (16) 6017
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34458
38.1%
n 32846
36.3%
r 3625
 
4.0%
f 2827
 
3.1%
i 2385
 
2.6%
t 2249
 
2.5%
a 1833
 
2.0%
s 1648
 
1.8%
j 1346
 
1.5%
d 1320
 
1.5%
Other values (16) 6017
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34458
38.1%
n 32846
36.3%
r 3625
 
4.0%
f 2827
 
3.1%
i 2385
 
2.6%
t 2249
 
2.5%
a 1833
 
2.0%
s 1648
 
1.8%
j 1346
 
1.5%
d 1320
 
1.5%
Other values (16) 6017
 
6.6%

final_rating
Real number (ℝ)

Missing  Zeros 

Distinct92
Distinct (%)0.2%
Missing4578
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean5.6249558
Minimum0
Maximum10
Zeros2936
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:32.248270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.9146519
Coefficient of variation (CV)0.34038524
Kurtosis2.5454047
Mean5.6249558
Median Absolute Deviation (MAD)0.9
Skewness-1.5248665
Sum254743
Variance3.6658921
MonotonicityNot monotonic
2025-03-26T17:49:32.390135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2936
 
5.9%
6 2458
 
4.9%
5 1993
 
4.0%
7 1881
 
3.8%
6.5 1720
 
3.4%
6.3 1600
 
3.2%
5.5 1379
 
2.8%
5.8 1367
 
2.7%
6.4 1346
 
2.7%
6.7 1336
 
2.7%
Other values (82) 27272
54.7%
(Missing) 4578
 
9.2%
ValueCountFrequency (%)
0 2936
5.9%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 184
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%
Distinct44730
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:32.856262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length72
Mean length14.370413
Min length1

Characters and Unicode

Total characters716595
Distinct characters2831
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40448 ?
Unique (%)81.1%

Sample

1st row!womenartrevolution
2nd row#1cheerleadercamp
3rd row#chicagogirl
4th row#horror
5th row#pellichoopulu
ValueCountFrequency (%)
aliceinwonderland 10
 
< 0.1%
lesmisérables 9
 
< 0.1%
cinderella 9
 
< 0.1%
home 8
 
< 0.1%
thethreemusketeers 8
 
< 0.1%
wutheringheights 7
 
< 0.1%
macbeth 7
 
< 0.1%
peterpan 7
 
< 0.1%
hamlet 7
 
< 0.1%
achristmascarol 7
 
< 0.1%
Other values (44696) 49830
99.8%
2025-03-26T17:49:33.576494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 80607
 
11.2%
a 59909
 
8.4%
t 50512
 
7.0%
o 48463
 
6.8%
i 47061
 
6.6%
n 45748
 
6.4%
r 45419
 
6.3%
s 41072
 
5.7%
l 34546
 
4.8%
h 30369
 
4.2%
Other values (2821) 232889
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 716595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 80607
 
11.2%
a 59909
 
8.4%
t 50512
 
7.0%
o 48463
 
6.8%
i 47061
 
6.6%
n 45748
 
6.4%
r 45419
 
6.3%
s 41072
 
5.7%
l 34546
 
4.8%
h 30369
 
4.2%
Other values (2821) 232889
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 716595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 80607
 
11.2%
a 59909
 
8.4%
t 50512
 
7.0%
o 48463
 
6.8%
i 47061
 
6.6%
n 45748
 
6.4%
r 45419
 
6.3%
s 41072
 
5.7%
l 34546
 
4.8%
h 30369
 
4.2%
Other values (2821) 232889
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 716595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 80607
 
11.2%
a 59909
 
8.4%
t 50512
 
7.0%
o 48463
 
6.8%
i 47061
 
6.6%
n 45748
 
6.4%
r 45419
 
6.3%
s 41072
 
5.7%
l 34546
 
4.8%
h 30369
 
4.2%
Other values (2821) 232889
32.5%
Distinct2388
Distinct (%)5.3%
Missing4578
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:34.101161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1039
Median length649
Mean length53.268879
Min length2

Characters and Unicode

Total characters2412441
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1768 ?
Unique (%)3.9%

Sample

1st row[]
2nd row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
3rd row[]
4th row[{'iso_3166_1': 'US', 'name': 'United States of America'}]
5th row[{'iso_3166_1': 'IN', 'name': 'India'}]
ValueCountFrequency (%)
iso_3166_1 49295
18.1%
name 49295
18.1%
united 25219
9.3%
states 21110
7.7%
of 21109
7.7%
america 21109
7.7%
us 21109
7.7%
6199
 
2.3%
gb 4082
 
1.5%
kingdom 4082
 
1.5%
Other values (341) 49991
18.3%
2025-03-26T17:49:34.879133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 394355
16.3%
227312
 
9.4%
e 129783
 
5.4%
a 119624
 
5.0%
i 107731
 
4.5%
_ 98590
 
4.1%
1 98590
 
4.1%
6 98590
 
4.1%
: 98590
 
4.1%
n 96683
 
4.0%
Other values (55) 942593
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2412441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 394355
16.3%
227312
 
9.4%
e 129783
 
5.4%
a 119624
 
5.0%
i 107731
 
4.5%
_ 98590
 
4.1%
1 98590
 
4.1%
6 98590
 
4.1%
: 98590
 
4.1%
n 96683
 
4.0%
Other values (55) 942593
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2412441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 394355
16.3%
227312
 
9.4%
e 129783
 
5.4%
a 119624
 
5.0%
i 107731
 
4.5%
_ 98590
 
4.1%
1 98590
 
4.1%
6 98590
 
4.1%
: 98590
 
4.1%
n 96683
 
4.0%
Other values (55) 942593
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2412441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 394355
16.3%
227312
 
9.4%
e 129783
 
5.4%
a 119624
 
5.0%
i 107731
 
4.5%
_ 98590
 
4.1%
1 98590
 
4.1%
6 98590
 
4.1%
: 98590
 
4.1%
n 96683
 
4.0%
Other values (55) 942593
39.1%

imdb_id
Text

Missing 

Distinct45287
Distinct (%)100.0%
Missing4579
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:35.321361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters407583
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45287 ?
Unique (%)100.0%

Sample

1st rowtt1699720
2nd rowtt1637976
3rd rowtt3060338
4th rowtt3526286
5th rowtt5824826
ValueCountFrequency (%)
tt1699720 1
 
< 0.1%
tt0094592 1
 
< 0.1%
tt0065360 1
 
< 0.1%
tt2258233 1
 
< 0.1%
tt3060338 1
 
< 0.1%
tt3526286 1
 
< 0.1%
tt5824826 1
 
< 0.1%
tt5335198 1
 
< 0.1%
tt2106284 1
 
< 0.1%
tt1024733 1
 
< 0.1%
Other values (45277) 45277
> 99.9%
2025-03-26T17:49:35.994726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 90574
22.2%
0 69728
17.1%
1 37099
9.1%
2 31112
 
7.6%
4 28394
 
7.0%
3 28012
 
6.9%
6 25362
 
6.2%
8 25325
 
6.2%
5 24174
 
5.9%
7 24130
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 407583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 90574
22.2%
0 69728
17.1%
1 37099
9.1%
2 31112
 
7.6%
4 28394
 
7.0%
3 28012
 
6.9%
6 25362
 
6.2%
8 25325
 
6.2%
5 24174
 
5.9%
7 24130
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 407583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 90574
22.2%
0 69728
17.1%
1 37099
9.1%
2 31112
 
7.6%
4 28394
 
7.0%
3 28012
 
6.9%
6 25362
 
6.2%
8 25325
 
6.2%
5 24174
 
5.9%
7 24130
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 407583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 90574
22.2%
0 69728
17.1%
1 37099
9.1%
2 31112
 
7.6%
4 28394
 
7.0%
3 28012
 
6.9%
6 25362
 
6.2%
8 25325
 
6.2%
5 24174
 
5.9%
7 24130
 
5.9%

production_companies
Text

Missing 

Distinct22670
Distinct (%)50.1%
Missing4578
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:36.563884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1252
Median length954
Mean length70.181638
Min length2

Characters and Unicode

Total characters3178386
Distinct characters293
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20327 ?
Unique (%)44.9%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[{'name': 'AST Studios', 'id': 75277}, {'name': 'Lowland Pictures', 'id': 75278}]
5th row[{'name': 'BigBen Films', 'id': 75835}, {'name': 'Vinoothna Geetha', 'id': 75836}]
ValueCountFrequency (%)
name 70357
 
17.6%
id 70357
 
17.6%
12613
 
3.2%
films 9432
 
2.4%
pictures 9247
 
2.3%
productions 9043
 
2.3%
film 6665
 
1.7%
entertainment 5139
 
1.3%
corporation 2187
 
0.5%
company 1762
 
0.4%
Other values (42149) 203289
50.8%
2025-03-26T17:49:37.260931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 421734
 
13.3%
354816
 
11.2%
i 177054
 
5.6%
e 164782
 
5.2%
n 160119
 
5.0%
a 147306
 
4.6%
: 140721
 
4.4%
m 114521
 
3.6%
, 107598
 
3.4%
d 103751
 
3.3%
Other values (283) 1285984
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3178386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 421734
 
13.3%
354816
 
11.2%
i 177054
 
5.6%
e 164782
 
5.2%
n 160119
 
5.0%
a 147306
 
4.6%
: 140721
 
4.4%
m 114521
 
3.6%
, 107598
 
3.4%
d 103751
 
3.3%
Other values (283) 1285984
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3178386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 421734
 
13.3%
354816
 
11.2%
i 177054
 
5.6%
e 164782
 
5.2%
n 160119
 
5.0%
a 147306
 
4.6%
: 140721
 
4.4%
m 114521
 
3.6%
, 107598
 
3.4%
d 103751
 
3.3%
Other values (283) 1285984
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3178386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 421734
 
13.3%
354816
 
11.2%
i 177054
 
5.6%
e 164782
 
5.2%
n 160119
 
5.0%
a 147306
 
4.6%
: 140721
 
4.4%
m 114521
 
3.6%
, 107598
 
3.4%
d 103751
 
3.3%
Other values (283) 1285984
40.5%
Distinct18155
Distinct (%)36.5%
Missing149
Missing (%)0.3%
Memory size389.7 KiB
Minimum1874-12-09 00:00:00
Maximum2068-12-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-26T17:49:37.459774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:37.782061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

final_year
Real number (ℝ)

Distinct182
Distinct (%)0.4%
Missing149
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1993.5121
Minimum1874
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.7 KiB
2025-03-26T17:49:37.923733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1942
Q11981
median2003
Q32011
95-th percentile2016
Maximum2068
Range194
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.87865
Coefficient of variation (CV)0.011978182
Kurtosis1.1391916
Mean1993.5121
Median Absolute Deviation (MAD)11
Skewness-1.2328701
Sum99111440
Variance570.18992
MonotonicityNot monotonic
2025-03-26T17:49:38.093339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 2163
 
4.3%
2015 2147
 
4.3%
2013 2047
 
4.1%
2012 1894
 
3.8%
2011 1848
 
3.7%
2016 1765
 
3.5%
2009 1745
 
3.5%
2010 1698
 
3.4%
2008 1647
 
3.3%
2007 1476
 
3.0%
Other values (172) 31287
62.7%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2068 8
< 0.1%
2067 9
< 0.1%
2066 6
< 0.1%
2065 7
< 0.1%
2064 8
< 0.1%
2063 8
< 0.1%
2062 8
< 0.1%
2061 5
< 0.1%
2060 5
< 0.1%
2059 4
< 0.1%

cast
Text

Missing 

Distinct42946
Distinct (%)94.8%
Missing4578
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:39.605471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60040
Median length8169
Mean length2398.365
Min length2

Characters and Unicode

Total characters108617152
Distinct characters729
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42945 ?
Unique (%)94.8%

Sample

1st row[{'cast_id': 1001, 'character': 'Herself', 'credit_id': '52fe48c3c3a36847f8178269', 'gender': 1, 'id': 213613, 'name': 'Lynn Hershman Leeson', 'order': 0, 'profile_path': '/jE1eBrttgiI6Hd21SkMRPaZ9K9v.jpg'}]
2nd row[{'cast_id': 1, 'character': 'Charlene Tilton', 'credit_id': '52fe45c4c3a36847f80d96e5', 'gender': 1, 'id': 21862, 'name': 'Charlene Tilton', 'order': 0, 'profile_path': '/jZwoQorUzQXTbwENKDCYPIbCGxl.jpg'}, {'cast_id': 2, 'character': 'Michael', 'credit_id': '52fe45c4c3a36847f80d96e9', 'gender': 2, 'id': 67154, 'name': 'Jay Gillespie', 'order': 1, 'profile_path': '/v5gOyjHBfUjh6yLEljUDAxrUFWu.jpg'}, {'cast_id': 3, 'character': 'Britt', 'credit_id': '52fe45c4c3a36847f80d96ed', 'gender': 1, 'id': 77756, 'name': 'Harmony Blossom', 'order': 2, 'profile_path': '/aTHBIbzxCVwAfBxXmnbDa5js1wU.jpg'}, {'cast_id': 9, 'character': 'Zan', 'credit_id': '5585a7e6c3a3682746000a53', 'gender': 2, 'id': 971237, 'name': 'Hector David Jr.', 'order': 3, 'profile_path': '/soCsXUn7OD4fDSDarFFlOxsyvFr.jpg'}]
3rd row[]
4th row[{'cast_id': 0, 'character': "Alex's 12-Step Friend", 'credit_id': '545bba84c3a3685358001b80', 'gender': 1, 'id': 343, 'name': 'Taryn Manning', 'order': 1, 'profile_path': '/ujPqJgRvOVaTgVI5qTWHBSM6BB3.jpg'}, {'cast_id': 1, 'character': "Sam's Mom", 'credit_id': '545bba8a0e0a261fad0023f6', 'gender': 1, 'id': 10871, 'name': 'Natasha Lyonne', 'order': 2, 'profile_path': '/8rhl25eCPQQFhv1Mvqe9eMP3MpS.jpg'}, {'cast_id': 2, 'character': 'Alex Cox', 'credit_id': '545bba8fc3a36853500018a4', 'gender': 1, 'id': 2838, 'name': 'Chloë Sevigny', 'order': 3, 'profile_path': '/qhVabrmmSwfhbApuagf0NlEhBaf.jpg'}, {'cast_id': 3, 'character': 'Mr. Cox', 'credit_id': '545bba94c3a3685353001a56', 'gender': 2, 'id': 9296, 'name': 'Balthazar Getty', 'order': 4, 'profile_path': '/F7k9JnUfbqIUvd124B0HQVT05h.jpg'}, {'cast_id': 4, 'character': 'Dr. White', 'credit_id': '545bba990e0a261fb900220b', 'gender': 2, 'id': 16327, 'name': 'Timothy Hutton', 'order': 5, 'profile_path': '/ypC4mzLExwmG0QHfi8vFe9LTeSi.jpg'}, {'cast_id': 5, 'character': 'Lisa', 'credit_id': '545bba9ec3a368535d001e67', 'gender': 1, 'id': 210573, 'name': 'Lydia Hearst', 'order': 6, 'profile_path': '/qtG7roaFf06dSX0BrgCNNcKtpfY.jpg'}, {'cast_id': 6, 'character': 'Mom', 'credit_id': '545bbaa4c3a368535d001e6b', 'gender': 0, 'id': 180425, 'name': 'Jessica Blank', 'order': 7, 'profile_path': '/4YXsdyncSuXGqeKysTVut2gjCCR.jpg'}, {'cast_id': 7, 'character': 'Molly', 'credit_id': '545bbab1c3a3685362001a0f', 'gender': 0, 'id': 1102331, 'name': 'Annabelle Dexter-Jones', 'order': 8, 'profile_path': '/iwze7ysPnKAjCXCtYXrep4MwVaF.jpg'}, {'cast_id': 15, 'character': 'Tatiana (voice)', 'credit_id': '572f9a8792514102ac000642', 'gender': 1, 'id': 61111, 'name': 'Tara Subkoff', 'order': 9, 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}]
5th row[{'cast_id': 0, 'character': 'Prashanth', 'credit_id': '5718614ac3a3687c2c00337c', 'gender': 2, 'id': 1508809, 'name': 'Vijay Deverakonda', 'order': 0, 'profile_path': '/7H4NUc9y5Cld0QZHigRVNWTYeA2.jpg'}, {'cast_id': 1, 'character': 'Chitra', 'credit_id': '5718615e925141282f003046', 'gender': 0, 'id': 1608576, 'name': 'Ritu Varma', 'order': 1, 'profile_path': '/hdQsWD26r5iOldntgOfDfmNct3z.jpg'}, {'cast_id': 11, 'character': '', 'credit_id': '5897a0adc3a3686d9e0001e1', 'gender': 0, 'id': 1441726, 'name': 'Nandoo', 'order': 2, 'profile_path': '/mhoSOfjGPB7pWSVpiAXLOUwNrmD.jpg'}, {'cast_id': 14, 'character': '', 'credit_id': '5897a1f7c3a36877150000c8', 'gender': 0, 'id': 1751708, 'name': 'Priyadarshi Pullikonda', 'order': 3, 'profile_path': '/32yf9gYAJjjRoeCJDY88qOGnJk.jpg'}, {'cast_id': 13, 'character': '', 'credit_id': '5897a18e9251417a7f000082', 'gender': 0, 'id': 1751705, 'name': 'Abhay Bethiganti', 'order': 4, 'profile_path': '/em7eqBETITCP0ZI8cEO5ae0dupt.jpg'}, {'cast_id': 9, 'character': '', 'credit_id': '589783a89251415aaa01727a', 'gender': 0, 'id': 1060294, 'name': 'Anish Kuruvilla', 'order': 5, 'profile_path': '/qtf9rH4b86YsF4ltLLd37Iz3QPK.jpg'}, {'cast_id': 12, 'character': '', 'credit_id': '5897a115c3a3687715000048', 'gender': 0, 'id': 101861, 'name': 'Gururaj Manepalli', 'order': 6, 'profile_path': '/ufJmM1tFTxpFlFuDCHA6QtpcRlF.jpg'}, {'cast_id': 15, 'character': '', 'credit_id': '5897a554c3a368771d000298', 'gender': 0, 'id': 1751737, 'name': 'Kedar Shankara', 'order': 7, 'profile_path': '/tq8jcMjVdAwsKJN8Gqc9ujD7ezR.jpg'}]
ValueCountFrequency (%)
character 561369
 
5.6%
name 561356
 
5.6%
order 561355
 
5.6%
id 561350
 
5.6%
gender 561345
 
5.6%
profile_path 561344
 
5.6%
credit_id 561344
 
5.6%
cast_id 561344
 
5.6%
2 292509
 
2.9%
0 262987
 
2.6%
Other values (1051839) 5047442
50.0%
2025-03-26T17:49:41.293000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 13104869
 
12.1%
10048630
 
9.3%
e 6289905
 
5.8%
r 5064494
 
4.7%
a 4859522
 
4.5%
: 4491198
 
4.1%
, 4454432
 
4.1%
d 4228537
 
3.9%
i 3883533
 
3.6%
c 3460903
 
3.2%
Other values (719) 48731129
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108617152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 13104869
 
12.1%
10048630
 
9.3%
e 6289905
 
5.8%
r 5064494
 
4.7%
a 4859522
 
4.5%
: 4491198
 
4.1%
, 4454432
 
4.1%
d 4228537
 
3.9%
i 3883533
 
3.6%
c 3460903
 
3.2%
Other values (719) 48731129
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108617152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 13104869
 
12.1%
10048630
 
9.3%
e 6289905
 
5.8%
r 5064494
 
4.7%
a 4859522
 
4.5%
: 4491198
 
4.1%
, 4454432
 
4.1%
d 4228537
 
3.9%
i 3883533
 
3.6%
c 3460903
 
3.2%
Other values (719) 48731129
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108617152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 13104869
 
12.1%
10048630
 
9.3%
e 6289905
 
5.8%
r 5064494
 
4.7%
a 4859522
 
4.5%
: 4491198
 
4.1%
, 4454432
 
4.1%
d 4228537
 
3.9%
i 3883533
 
3.6%
c 3460903
 
3.2%
Other values (719) 48731129
44.9%

crew
Text

Missing 

Distinct44568
Distinct (%)98.4%
Missing4578
Missing (%)9.2%
Memory size389.7 KiB
2025-03-26T17:49:42.331540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length73601
Median length14058
Mean length1769.8422
Min length2

Characters and Unicode

Total characters80152613
Distinct characters364
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44567 ?
Unique (%)98.4%

Sample

1st row[{'credit_id': '52fe48c3c3a36847f8178265', 'department': 'Directing', 'gender': 1, 'id': 213613, 'job': 'Director', 'name': 'Lynn Hershman Leeson', 'profile_path': '/jE1eBrttgiI6Hd21SkMRPaZ9K9v.jpg'}, {'credit_id': '557ef4ed9251410a2c002056', 'department': 'Production', 'gender': 0, 'id': 1475661, 'job': 'Producer', 'name': 'Ariel Dougherty', 'profile_path': '/hhEnbQQ7jFlqMPf6p5lTwnAbop1.jpg'}]
2nd row[{'credit_id': '52fe45c4c3a36847f80d96f3', 'department': 'Directing', 'gender': 2, 'id': 128762, 'job': 'Director', 'name': 'Mark Quod', 'profile_path': None}, {'credit_id': '52fe45c4c3a36847f80d96f9', 'department': 'Production', 'gender': 2, 'id': 30053, 'job': 'Producer', 'name': 'David Michael Latt', 'profile_path': None}, {'credit_id': '52fe45c4c3a36847f80d9705', 'department': 'Sound', 'gender': 2, 'id': 128763, 'job': 'Original Music Composer', 'name': 'Chris Ridenhour', 'profile_path': None}, {'credit_id': '538b2e14c3a3687155000f8a', 'department': 'Production', 'gender': 2, 'id': 236844, 'job': 'Executive Producer', 'name': 'David Rimawi', 'profile_path': None}, {'credit_id': '56a7d89fc3a36828af001e64', 'department': 'Writing', 'gender': 1, 'id': 146005, 'job': 'Writer', 'name': 'Naomi L. Selfman', 'profile_path': None}]
3rd row[{'credit_id': '5362c9a7c3a368156f00049f', 'department': 'Directing', 'gender': 2, 'id': 50584, 'job': 'Director', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9ebc3a368158d0004c1', 'department': 'Editing', 'gender': 2, 'id': 50584, 'job': 'Editor', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9cec3a368157600044f', 'department': 'Production', 'gender': 2, 'id': 50584, 'job': 'Producer', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9bbc3a368156f0004a1', 'department': 'Writing', 'gender': 2, 'id': 50584, 'job': 'Writer', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9fbc3a368157d000448', 'department': 'Production', 'gender': 2, 'id': 1099063, 'job': 'Producer', 'name': 'Mark Rinehart', 'profile_path': None}, {'credit_id': '5362ca0fc3a36815950004c7', 'department': 'Editing', 'gender': 0, 'id': 1099064, 'job': 'Editor', 'name': 'Jeff Castelluccio', 'profile_path': None}]
4th row[{'credit_id': '545bbabf0e0a261fb9002212', 'department': 'Directing', 'gender': 1, 'id': 61111, 'job': 'Director', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbac70e0a261fb6002329', 'department': 'Writing', 'gender': 1, 'id': 61111, 'job': 'Screenplay', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbad3c3a3685358001b92', 'department': 'Production', 'gender': 0, 'id': 1382445, 'job': 'Producer', 'name': 'Jason Ludman', 'profile_path': None}, {'credit_id': '545bbadbc3a368535d001e74', 'department': 'Production', 'gender': 0, 'id': 1382446, 'job': 'Producer', 'name': 'Oren Segal', 'profile_path': None}, {'credit_id': '545bbae4c3a36853500018a8', 'department': 'Production', 'gender': 1, 'id': 61111, 'job': 'Producer', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbaf3c3a3685358001b9d', 'department': 'Production', 'gender': 0, 'id': 1382448, 'job': 'Producer', 'name': 'Brendan Walsh', 'profile_path': None}]
5th row[{'credit_id': '571862bbc3a3687fd60029dd', 'department': 'Camera', 'gender': 0, 'id': 1608578, 'job': 'Director of Photography', 'name': 'Nagesh Bannel', 'profile_path': None}, {'credit_id': '571862f5c3a3687bc7002af5', 'department': 'Sound', 'gender': 0, 'id': 1608580, 'job': 'Music Director', 'name': 'Vivek Saagar', 'profile_path': None}, {'credit_id': '58979fae925141730000017e', 'department': 'Directing', 'gender': 0, 'id': 1673599, 'job': 'Director', 'name': 'Tharun Bhascker Dhaassyam', 'profile_path': None}, {'credit_id': '584ad597c3a368141a021fb3', 'department': 'Writing', 'gender': 0, 'id': 1673599, 'job': 'Writer', 'name': 'Tharun Bhascker Dhaassyam', 'profile_path': None}, {'credit_id': '584acff7c3a368142801db85', 'department': 'Production', 'gender': 0, 'id': 1673602, 'job': 'Producer', 'name': 'Yash Rangineni', 'profile_path': None}, {'credit_id': '584acfe092514119bf01fee6', 'department': 'Production', 'gender': 0, 'id': 1720222, 'job': 'Producer', 'name': 'Raj Kandukuri', 'profile_path': None}]
ValueCountFrequency (%)
department 467071
 
6.3%
job 463124
 
6.3%
name 463094
 
6.3%
gender 463094
 
6.3%
profile_path 463094
 
6.3%
id 463094
 
6.3%
credit_id 463094
 
6.3%
none 368196
 
5.0%
0 271544
 
3.7%
2 160523
 
2.2%
Other values (728059) 3333506
45.2%
2025-03-26T17:49:43.577262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 10375307
 
12.9%
7334210
 
9.2%
e 5437896
 
6.8%
r 3379503
 
4.2%
: 3241660
 
4.0%
i 3215013
 
4.0%
, 3197238
 
4.0%
d 3156032
 
3.9%
t 2922853
 
3.6%
a 2899567
 
3.6%
Other values (354) 34993334
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80152613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 10375307
 
12.9%
7334210
 
9.2%
e 5437896
 
6.8%
r 3379503
 
4.2%
: 3241660
 
4.0%
i 3215013
 
4.0%
, 3197238
 
4.0%
d 3156032
 
3.9%
t 2922853
 
3.6%
a 2899567
 
3.6%
Other values (354) 34993334
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80152613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 10375307
 
12.9%
7334210
 
9.2%
e 5437896
 
6.8%
r 3379503
 
4.2%
: 3241660
 
4.0%
i 3215013
 
4.0%
, 3197238
 
4.0%
d 3156032
 
3.9%
t 2922853
 
3.6%
a 2899567
 
3.6%
Other values (354) 34993334
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80152613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 10375307
 
12.9%
7334210
 
9.2%
e 5437896
 
6.8%
r 3379503
 
4.2%
: 3241660
 
4.0%
i 3215013
 
4.0%
, 3197238
 
4.0%
d 3156032
 
3.9%
t 2922853
 
3.6%
a 2899567
 
3.6%
Other values (354) 34993334
43.7%

director
Text

Missing 

Distinct12011
Distinct (%)44.9%
Missing23137
Missing (%)46.4%
Memory size389.7 KiB
2025-03-26T17:49:44.119313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length923
Median length477
Mean length15.167159
Min length3

Characters and Unicode

Total characters405403
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7825 ?
Unique (%)29.3%

Sample

1st rowTara Subkoff
2nd rowTharun Bhascker Dhaassyam
3rd rowLyubomyr Levytsky
4th rowSeth Grossman
5th rowTatia Rosenthal
ValueCountFrequency (%)
john 851
 
1.4%
robert 568
 
0.9%
michael 556
 
0.9%
david 523
 
0.8%
william 395
 
0.6%
richard 366
 
0.6%
peter 327
 
0.5%
george 307
 
0.5%
james 306
 
0.5%
paul 273
 
0.4%
Other values (13024) 57592
92.8%
2025-03-26T17:49:44.772794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35335
 
8.7%
a 34394
 
8.5%
e 33912
 
8.4%
r 26624
 
6.6%
n 26623
 
6.6%
i 25711
 
6.3%
o 23667
 
5.8%
l 18108
 
4.5%
s 13653
 
3.4%
t 13045
 
3.2%
Other values (88) 154331
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 405403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35335
 
8.7%
a 34394
 
8.5%
e 33912
 
8.4%
r 26624
 
6.6%
n 26623
 
6.6%
i 25711
 
6.3%
o 23667
 
5.8%
l 18108
 
4.5%
s 13653
 
3.4%
t 13045
 
3.2%
Other values (88) 154331
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 405403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35335
 
8.7%
a 34394
 
8.5%
e 33912
 
8.4%
r 26624
 
6.6%
n 26623
 
6.6%
i 25711
 
6.3%
o 23667
 
5.8%
l 18108
 
4.5%
s 13653
 
3.4%
t 13045
 
3.2%
Other values (88) 154331
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 405403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35335
 
8.7%
a 34394
 
8.5%
e 33912
 
8.4%
r 26624
 
6.6%
n 26623
 
6.6%
i 25711
 
6.3%
o 23667
 
5.8%
l 18108
 
4.5%
s 13653
 
3.4%
t 13045
 
3.2%
Other values (88) 154331
38.1%

director_id
Text

Missing 

Distinct11216
Distinct (%)42.0%
Missing23137
Missing (%)46.4%
Memory size389.7 KiB
2025-03-26T17:49:45.146318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length16.000374
Min length16

Characters and Unicode

Total characters427674
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6867 ?
Unique (%)25.7%

Sample

1st row/name/nm0836964/
2nd row/name/nm5056902/
3rd row/name/nm2463500/
4th row/name/nm0343713/
5th row/name/nm1038127/
ValueCountFrequency (%)
name/nm0002031 49
 
0.2%
name/nm0000033 48
 
0.2%
name/nm0000406 45
 
0.2%
name/nm0861703 36
 
0.1%
name/nm0000485 34
 
0.1%
name/nm0368871 34
 
0.1%
name/nm0586281 34
 
0.1%
name/nm0936404 33
 
0.1%
name/nm0909825 33
 
0.1%
name/nm0496746 33
 
0.1%
Other values (11206) 26350
98.6%
2025-03-26T17:49:45.558012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 80187
18.7%
n 53458
12.5%
m 53458
12.5%
0 47312
11.1%
a 26729
 
6.2%
e 26729
 
6.2%
1 18790
 
4.4%
2 16788
 
3.9%
3 15641
 
3.7%
4 15590
 
3.6%
Other values (5) 72992
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 427674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 80187
18.7%
n 53458
12.5%
m 53458
12.5%
0 47312
11.1%
a 26729
 
6.2%
e 26729
 
6.2%
1 18790
 
4.4%
2 16788
 
3.9%
3 15641
 
3.7%
4 15590
 
3.6%
Other values (5) 72992
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 427674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 80187
18.7%
n 53458
12.5%
m 53458
12.5%
0 47312
11.1%
a 26729
 
6.2%
e 26729
 
6.2%
1 18790
 
4.4%
2 16788
 
3.9%
3 15641
 
3.7%
4 15590
 
3.6%
Other values (5) 72992
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 427674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 80187
18.7%
n 53458
12.5%
m 53458
12.5%
0 47312
11.1%
a 26729
 
6.2%
e 26729
 
6.2%
1 18790
 
4.4%
2 16788
 
3.9%
3 15641
 
3.7%
4 15590
 
3.6%
Other values (5) 72992
17.1%

star
Text

Missing 

Distinct26611
Distinct (%)99.6%
Missing23147
Missing (%)46.4%
Memory size389.7 KiB
2025-03-26T17:49:46.059143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length93
Mean length61.814439
Min length10

Characters and Unicode

Total characters1651620
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26526 ?
Unique (%)99.3%

Sample

1st rowSadie Seelert, Haley Murphy, Bridget McGarry, Blue Lindeberg
2nd rowAbhay Bethiganti, Keshav Deepak, Vijay Deverakonda, Anish Kuruvilla
3rd rowSergei Zorenko, Boris Savenko, Kseniya Mishina, Arthur Shurypa
4th rowRoss Patterson, Jessie Wiseman, Seth Grossman, Lauren Aboulafia
5th rowGeoffrey Rush, Anthony LaPaglia, Samuel Johnson, Barry Otto
ValueCountFrequency (%)
john 1708
 
0.8%
michael 1351
 
0.6%
robert 1192
 
0.5%
james 1129
 
0.5%
david 971
 
0.4%
richard 910
 
0.4%
lee 737
 
0.3%
peter 719
 
0.3%
paul 667
 
0.3%
william 604
 
0.3%
Other values (35645) 209369
95.4%
2025-03-26T17:49:46.820769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192638
 
11.7%
a 136334
 
8.3%
e 124873
 
7.6%
n 100683
 
6.1%
r 92549
 
5.6%
i 91440
 
5.5%
79995
 
4.8%
, 79995
 
4.8%
o 79265
 
4.8%
l 66874
 
4.0%
Other values (97) 606974
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1651620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
192638
 
11.7%
a 136334
 
8.3%
e 124873
 
7.6%
n 100683
 
6.1%
r 92549
 
5.6%
i 91440
 
5.5%
79995
 
4.8%
, 79995
 
4.8%
o 79265
 
4.8%
l 66874
 
4.0%
Other values (97) 606974
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1651620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
192638
 
11.7%
a 136334
 
8.3%
e 124873
 
7.6%
n 100683
 
6.1%
r 92549
 
5.6%
i 91440
 
5.5%
79995
 
4.8%
, 79995
 
4.8%
o 79265
 
4.8%
l 66874
 
4.0%
Other values (97) 606974
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1651620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
192638
 
11.7%
a 136334
 
8.3%
e 124873
 
7.6%
n 100683
 
6.1%
r 92549
 
5.6%
i 91440
 
5.5%
79995
 
4.8%
, 79995
 
4.8%
o 79265
 
4.8%
l 66874
 
4.0%
Other values (97) 606974
36.8%

star_id
Text

Missing 

Distinct26627
Distinct (%)99.6%
Missing23144
Missing (%)46.4%
Memory size389.7 KiB
2025-03-26T17:49:47.114050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1053
Median length67
Mean length68.680301
Min length16

Characters and Unicode

Total characters1835275
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26551 ?
Unique (%)99.4%

Sample

1st row/name/nm6344380/,/name/nm4239131/,/name/nm5376482/,/name/nm6344381/
2nd row/name/nm8232982/,/name/nm6642302/,/name/nm4797922/,/name/nm2378839/
3rd row/name/nm7881765/,/name/nm7881762/,/name/nm2432008/,/name/nm7881761/
4th row/name/nm0666388/,/name/nm3034435/,/name/nm0343713/,/name/nm2992430/
5th row/name/nm0001691/,/name/nm0001439/,/name/nm0426170/,/name/nm0653222/
ValueCountFrequency (%)
name/nm0000638/,/name/nm0000559/,/name/nm0001420/,/name/nm0001150 6
 
< 0.1%
name/nm5752523/,/name/nm0424453/,/name/nm3940484/,/name/nm1870939 4
 
< 0.1%
name/nm0000230/,/name/nm0001735/,/name/nm0949350/,/name/nm0001835 4
 
< 0.1%
name/nm0793840/,/name/nm0436778/,/name/nm0757327/,/name/nm0757087/,/name/nm0394679 3
 
< 0.1%
name/nm0793840/,/name/nm2976492/,/name/nm1543455/,/name/nm1543296/,/name/nm0594646 3
 
< 0.1%
name/nm0802325/,/name/nm0482121/,/name/nm0799982/,/name/nm1230870 3
 
< 0.1%
name/nm0395950/,/name/nm1543455/,/name/nm0436778/,/name/nm0837358 3
 
< 0.1%
name/nm0000858/,/name/nm0397397/,/name/nm0132682/,/name/nm0219666 3
 
< 0.1%
name/nm0905152/,/name/nm0000206/,/name/nm0000401/,/name/nm0005251/,/name/nm0915989 3
 
< 0.1%
name/nm0819825/,/name/nm0344655/,/name/nm0120381/,/name/nm0910432 3
 
< 0.1%
Other values (26617) 26687
99.9%
2025-03-26T17:49:47.592551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 328557
17.9%
n 219038
11.9%
m 219038
11.9%
0 217441
11.8%
a 109519
 
6.0%
e 109519
 
6.0%
, 82797
 
4.5%
1 78689
 
4.3%
2 65137
 
3.5%
4 61765
 
3.4%
Other values (6) 343775
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1835275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 328557
17.9%
n 219038
11.9%
m 219038
11.9%
0 217441
11.8%
a 109519
 
6.0%
e 109519
 
6.0%
, 82797
 
4.5%
1 78689
 
4.3%
2 65137
 
3.5%
4 61765
 
3.4%
Other values (6) 343775
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1835275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 328557
17.9%
n 219038
11.9%
m 219038
11.9%
0 217441
11.8%
a 109519
 
6.0%
e 109519
 
6.0%
, 82797
 
4.5%
1 78689
 
4.3%
2 65137
 
3.5%
4 61765
 
3.4%
Other values (6) 343775
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1835275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 328557
17.9%
n 219038
11.9%
m 219038
11.9%
0 217441
11.8%
a 109519
 
6.0%
e 109519
 
6.0%
, 82797
 
4.5%
1 78689
 
4.3%
2 65137
 
3.5%
4 61765
 
3.4%
Other values (6) 343775
18.7%

_merge
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
left_only
43266 
right_only
4578 
both
 
2022

Length

Max length10
Median length9
Mean length8.8890627
Min length4

Characters and Unicode

Total characters443262
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowleft_only
2nd rowleft_only
3rd rowleft_only
4th rowboth
5th rowleft_only

Common Values

ValueCountFrequency (%)
left_only 43266
86.8%
right_only 4578
 
9.2%
both 2022
 
4.1%

Length

2025-03-26T17:49:47.711144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T17:49:47.825668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
left_only 43266
86.8%
right_only 4578
 
9.2%
both 2022
 
4.1%

Most occurring characters

ValueCountFrequency (%)
l 91110
20.6%
t 49866
11.2%
o 49866
11.2%
_ 47844
10.8%
n 47844
10.8%
y 47844
10.8%
e 43266
9.8%
f 43266
9.8%
h 6600
 
1.5%
r 4578
 
1.0%
Other values (3) 11178
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 91110
20.6%
t 49866
11.2%
o 49866
11.2%
_ 47844
10.8%
n 47844
10.8%
y 47844
10.8%
e 43266
9.8%
f 43266
9.8%
h 6600
 
1.5%
r 4578
 
1.0%
Other values (3) 11178
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 91110
20.6%
t 49866
11.2%
o 49866
11.2%
_ 47844
10.8%
n 47844
10.8%
y 47844
10.8%
e 43266
9.8%
f 43266
9.8%
h 6600
 
1.5%
r 4578
 
1.0%
Other values (3) 11178
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 91110
20.6%
t 49866
11.2%
o 49866
11.2%
_ 47844
10.8%
n 47844
10.8%
y 47844
10.8%
e 43266
9.8%
f 43266
9.8%
h 6600
 
1.5%
r 4578
 
1.0%
Other values (3) 11178
 
2.5%

Interactions

2025-03-26T17:49:17.022986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:06.360892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:08.548624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:11.004697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:13.353887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:15.560097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:17.241832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:06.647515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:08.979482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:11.845821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:13.722579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:15.893764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:17.485191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:06.923180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:09.400678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:12.178122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:14.102195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:16.122730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:17.818229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:07.319900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:09.761684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:12.527575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:14.425520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:16.339916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:18.265573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:07.761172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:10.195557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:12.800989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:14.788887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:16.593875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:18.658216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:08.148478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:10.560991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:13.019332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:15.174273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-26T17:49:16.800475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-26T17:49:47.896819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
_mergefinal_budgetfinal_certificatefinal_domestic_boxofficefinal_ratingfinal_runtimefinal_worldwide_boxofficefinal_year
_merge1.0000.1130.2270.0970.0840.0460.0570.206
final_budget0.1131.0000.1260.6650.0470.3670.7250.130
final_certificate0.2270.1261.0000.0670.0690.0850.0650.403
final_domestic_boxoffice0.0970.6650.0671.0000.0910.1990.915-0.036
final_rating0.0840.0470.0690.0911.0000.1810.120-0.008
final_runtime0.0460.3670.0850.1990.1811.0000.2550.065
final_worldwide_boxoffice0.0570.7250.0650.9150.1200.2551.0000.024
final_year0.2060.1300.403-0.036-0.0080.0650.0241.000

Missing values

2025-03-26T17:49:19.513004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-26T17:49:20.909747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-26T17:49:22.712216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

final_titlefinal_budgetfinal_worldwide_boxofficefinal_domestic_boxofficefinal_genresfinal_runtimefinal_overviewfinal_certificatefinal_original_languagefinal_ratingfinal_clean_titlefinal_production_countriesimdb_idproduction_companiesrelease_datefinal_yearcastcrewdirectordirector_idstarstar_id_merge
0!Women Art RevolutionNaNNaNNaN[{'id': 99, 'name': 'Documentary'}]83.0Through intimate interviews, provocative art, and rare, historical film and video footage, this feature documentary reveals how art addressing political consequences of discrimination and violence, the Feminist Art Revolution radically transformed the art and culture of our times.NaNen4.3!womenartrevolution[]tt1699720[]2010-01-012010.0[{'cast_id': 1001, 'character': 'Herself', 'credit_id': '52fe48c3c3a36847f8178269', 'gender': 1, 'id': 213613, 'name': 'Lynn Hershman Leeson', 'order': 0, 'profile_path': '/jE1eBrttgiI6Hd21SkMRPaZ9K9v.jpg'}][{'credit_id': '52fe48c3c3a36847f8178265', 'department': 'Directing', 'gender': 1, 'id': 213613, 'job': 'Director', 'name': 'Lynn Hershman Leeson', 'profile_path': '/jE1eBrttgiI6Hd21SkMRPaZ9K9v.jpg'}, {'credit_id': '557ef4ed9251410a2c002056', 'department': 'Production', 'gender': 0, 'id': 1475661, 'job': 'Producer', 'name': 'Ariel Dougherty', 'profile_path': '/hhEnbQQ7jFlqMPf6p5lTwnAbop1.jpg'}]NaNNaNNaNNaNleft_only
1#1 Cheerleader CampNaNNaNNaN[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}]95.0A pair of horny college guys get summer jobs at a sexy cheerleader camp.NaNen3.4#1cheerleadercamp[{'iso_3166_1': 'US', 'name': 'United States of America'}]tt1637976[]2010-07-272010.0[{'cast_id': 1, 'character': 'Charlene Tilton', 'credit_id': '52fe45c4c3a36847f80d96e5', 'gender': 1, 'id': 21862, 'name': 'Charlene Tilton', 'order': 0, 'profile_path': '/jZwoQorUzQXTbwENKDCYPIbCGxl.jpg'}, {'cast_id': 2, 'character': 'Michael', 'credit_id': '52fe45c4c3a36847f80d96e9', 'gender': 2, 'id': 67154, 'name': 'Jay Gillespie', 'order': 1, 'profile_path': '/v5gOyjHBfUjh6yLEljUDAxrUFWu.jpg'}, {'cast_id': 3, 'character': 'Britt', 'credit_id': '52fe45c4c3a36847f80d96ed', 'gender': 1, 'id': 77756, 'name': 'Harmony Blossom', 'order': 2, 'profile_path': '/aTHBIbzxCVwAfBxXmnbDa5js1wU.jpg'}, {'cast_id': 9, 'character': 'Zan', 'credit_id': '5585a7e6c3a3682746000a53', 'gender': 2, 'id': 971237, 'name': 'Hector David Jr.', 'order': 3, 'profile_path': '/soCsXUn7OD4fDSDarFFlOxsyvFr.jpg'}][{'credit_id': '52fe45c4c3a36847f80d96f3', 'department': 'Directing', 'gender': 2, 'id': 128762, 'job': 'Director', 'name': 'Mark Quod', 'profile_path': None}, {'credit_id': '52fe45c4c3a36847f80d96f9', 'department': 'Production', 'gender': 2, 'id': 30053, 'job': 'Producer', 'name': 'David Michael Latt', 'profile_path': None}, {'credit_id': '52fe45c4c3a36847f80d9705', 'department': 'Sound', 'gender': 2, 'id': 128763, 'job': 'Original Music Composer', 'name': 'Chris Ridenhour', 'profile_path': None}, {'credit_id': '538b2e14c3a3687155000f8a', 'department': 'Production', 'gender': 2, 'id': 236844, 'job': 'Executive Producer', 'name': 'David Rimawi', 'profile_path': None}, {'credit_id': '56a7d89fc3a36828af001e64', 'department': 'Writing', 'gender': 1, 'id': 146005, 'job': 'Writer', 'name': 'Naomi L. Selfman', 'profile_path': None}]NaNNaNNaNNaNleft_only
2#chicagoGirlNaNNaNNaN[{'id': 99, 'name': 'Documentary'}]74.0From her childhood bedroom in the Chicago suburbs, an American teenage girl uses social media to run the revolution in Syria. Armed with Facebook, Twitter, Skype and cameraphones, she helps her social network in Damascus and Homs braves snipers and shelling in the streets and the world the human rights atrocities of one of the most brutal dictators. But as the revolution rages on, everyone in the network must decide what is the most effective way to fight a dictator: social media or AK-47s.NaNen7.0#chicagogirl[]tt3060338[]2013-11-212013.0[][{'credit_id': '5362c9a7c3a368156f00049f', 'department': 'Directing', 'gender': 2, 'id': 50584, 'job': 'Director', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9ebc3a368158d0004c1', 'department': 'Editing', 'gender': 2, 'id': 50584, 'job': 'Editor', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9cec3a368157600044f', 'department': 'Production', 'gender': 2, 'id': 50584, 'job': 'Producer', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9bbc3a368156f0004a1', 'department': 'Writing', 'gender': 2, 'id': 50584, 'job': 'Writer', 'name': 'Joe Piscatella', 'profile_path': None}, {'credit_id': '5362c9fbc3a368157d000448', 'department': 'Production', 'gender': 2, 'id': 1099063, 'job': 'Producer', 'name': 'Mark Rinehart', 'profile_path': None}, {'credit_id': '5362ca0fc3a36815950004c7', 'department': 'Editing', 'gender': 0, 'id': 1099064, 'job': 'Editor', 'name': 'Jeff Castelluccio', 'profile_path': None}]NaNNaNNaNNaNleft_only
3#Horror1500000.00.00.0[{'id': 18, 'name': 'Drama'}, {'id': 9648, 'name': 'Mystery'}, {'id': 27, 'name': 'Horror'}, {'id': 53, 'name': 'Thriller'}]90.0Inspired by actual events, a group of 12 year old girls face a night of horror when the compulsive addiction of an online social media game turns a moment of cyber bullying into a night of insanity.Not Ratedde3.4#horror[{'iso_3166_1': 'US', 'name': 'United States of America'}]tt3526286[{'name': 'AST Studios', 'id': 75277}, {'name': 'Lowland Pictures', 'id': 75278}]2015-11-202015.0[{'cast_id': 0, 'character': "Alex's 12-Step Friend", 'credit_id': '545bba84c3a3685358001b80', 'gender': 1, 'id': 343, 'name': 'Taryn Manning', 'order': 1, 'profile_path': '/ujPqJgRvOVaTgVI5qTWHBSM6BB3.jpg'}, {'cast_id': 1, 'character': "Sam's Mom", 'credit_id': '545bba8a0e0a261fad0023f6', 'gender': 1, 'id': 10871, 'name': 'Natasha Lyonne', 'order': 2, 'profile_path': '/8rhl25eCPQQFhv1Mvqe9eMP3MpS.jpg'}, {'cast_id': 2, 'character': 'Alex Cox', 'credit_id': '545bba8fc3a36853500018a4', 'gender': 1, 'id': 2838, 'name': 'Chloë Sevigny', 'order': 3, 'profile_path': '/qhVabrmmSwfhbApuagf0NlEhBaf.jpg'}, {'cast_id': 3, 'character': 'Mr. Cox', 'credit_id': '545bba94c3a3685353001a56', 'gender': 2, 'id': 9296, 'name': 'Balthazar Getty', 'order': 4, 'profile_path': '/F7k9JnUfbqIUvd124B0HQVT05h.jpg'}, {'cast_id': 4, 'character': 'Dr. White', 'credit_id': '545bba990e0a261fb900220b', 'gender': 2, 'id': 16327, 'name': 'Timothy Hutton', 'order': 5, 'profile_path': '/ypC4mzLExwmG0QHfi8vFe9LTeSi.jpg'}, {'cast_id': 5, 'character': 'Lisa', 'credit_id': '545bba9ec3a368535d001e67', 'gender': 1, 'id': 210573, 'name': 'Lydia Hearst', 'order': 6, 'profile_path': '/qtG7roaFf06dSX0BrgCNNcKtpfY.jpg'}, {'cast_id': 6, 'character': 'Mom', 'credit_id': '545bbaa4c3a368535d001e6b', 'gender': 0, 'id': 180425, 'name': 'Jessica Blank', 'order': 7, 'profile_path': '/4YXsdyncSuXGqeKysTVut2gjCCR.jpg'}, {'cast_id': 7, 'character': 'Molly', 'credit_id': '545bbab1c3a3685362001a0f', 'gender': 0, 'id': 1102331, 'name': 'Annabelle Dexter-Jones', 'order': 8, 'profile_path': '/iwze7ysPnKAjCXCtYXrep4MwVaF.jpg'}, {'cast_id': 15, 'character': 'Tatiana (voice)', 'credit_id': '572f9a8792514102ac000642', 'gender': 1, 'id': 61111, 'name': 'Tara Subkoff', 'order': 9, 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}][{'credit_id': '545bbabf0e0a261fb9002212', 'department': 'Directing', 'gender': 1, 'id': 61111, 'job': 'Director', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbac70e0a261fb6002329', 'department': 'Writing', 'gender': 1, 'id': 61111, 'job': 'Screenplay', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbad3c3a3685358001b92', 'department': 'Production', 'gender': 0, 'id': 1382445, 'job': 'Producer', 'name': 'Jason Ludman', 'profile_path': None}, {'credit_id': '545bbadbc3a368535d001e74', 'department': 'Production', 'gender': 0, 'id': 1382446, 'job': 'Producer', 'name': 'Oren Segal', 'profile_path': None}, {'credit_id': '545bbae4c3a36853500018a8', 'department': 'Production', 'gender': 1, 'id': 61111, 'job': 'Producer', 'name': 'Tara Subkoff', 'profile_path': '/qBbSymmytAOqgAGlll3sN5LXih3.jpg'}, {'credit_id': '545bbaf3c3a3685358001b9d', 'department': 'Production', 'gender': 0, 'id': 1382448, 'job': 'Producer', 'name': 'Brendan Walsh', 'profile_path': None}]Tara Subkoff/name/nm0836964/Sadie Seelert, \nHaley Murphy, \nBridget McGarry, \nBlue Lindeberg/name/nm6344380/,/name/nm4239131/,/name/nm5376482/,/name/nm6344381/both
4#Pellichoopulu200000.05500000.0NaN[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]124.0Raj Kandukuri and BiG Ben Films in association with Vinoothna Geetha presents #Pellichoopulu, a Telugu romantic comedy about two individuals Prashanth and Chitra who meet at a 'Pellichoopulu'. The story is about a modern day couple anchored down by their traditional roots. When fate plays havoc in their lives, they set off on a journey to find their dreams, face their fears and ultimately find true love. Directed by Tharun Bhascker of Anukokunda and Sainma fame, the film stars Vijay Deverakonda and Ritu Varma.NaNte7.8#pellichoopulu[{'iso_3166_1': 'IN', 'name': 'India'}]tt5824826[{'name': 'BigBen Films', 'id': 75835}, {'name': 'Vinoothna Geetha', 'id': 75836}]2016-07-292016.0[{'cast_id': 0, 'character': 'Prashanth', 'credit_id': '5718614ac3a3687c2c00337c', 'gender': 2, 'id': 1508809, 'name': 'Vijay Deverakonda', 'order': 0, 'profile_path': '/7H4NUc9y5Cld0QZHigRVNWTYeA2.jpg'}, {'cast_id': 1, 'character': 'Chitra', 'credit_id': '5718615e925141282f003046', 'gender': 0, 'id': 1608576, 'name': 'Ritu Varma', 'order': 1, 'profile_path': '/hdQsWD26r5iOldntgOfDfmNct3z.jpg'}, {'cast_id': 11, 'character': '', 'credit_id': '5897a0adc3a3686d9e0001e1', 'gender': 0, 'id': 1441726, 'name': 'Nandoo', 'order': 2, 'profile_path': '/mhoSOfjGPB7pWSVpiAXLOUwNrmD.jpg'}, {'cast_id': 14, 'character': '', 'credit_id': '5897a1f7c3a36877150000c8', 'gender': 0, 'id': 1751708, 'name': 'Priyadarshi Pullikonda', 'order': 3, 'profile_path': '/32yf9gYAJjjRoeCJDY88qOGnJk.jpg'}, {'cast_id': 13, 'character': '', 'credit_id': '5897a18e9251417a7f000082', 'gender': 0, 'id': 1751705, 'name': 'Abhay Bethiganti', 'order': 4, 'profile_path': '/em7eqBETITCP0ZI8cEO5ae0dupt.jpg'}, {'cast_id': 9, 'character': '', 'credit_id': '589783a89251415aaa01727a', 'gender': 0, 'id': 1060294, 'name': 'Anish Kuruvilla', 'order': 5, 'profile_path': '/qtf9rH4b86YsF4ltLLd37Iz3QPK.jpg'}, {'cast_id': 12, 'character': '', 'credit_id': '5897a115c3a3687715000048', 'gender': 0, 'id': 101861, 'name': 'Gururaj Manepalli', 'order': 6, 'profile_path': '/ufJmM1tFTxpFlFuDCHA6QtpcRlF.jpg'}, {'cast_id': 15, 'character': '', 'credit_id': '5897a554c3a368771d000298', 'gender': 0, 'id': 1751737, 'name': 'Kedar Shankara', 'order': 7, 'profile_path': '/tq8jcMjVdAwsKJN8Gqc9ujD7ezR.jpg'}][{'credit_id': '571862bbc3a3687fd60029dd', 'department': 'Camera', 'gender': 0, 'id': 1608578, 'job': 'Director of Photography', 'name': 'Nagesh Bannel', 'profile_path': None}, {'credit_id': '571862f5c3a3687bc7002af5', 'department': 'Sound', 'gender': 0, 'id': 1608580, 'job': 'Music Director', 'name': 'Vivek Saagar', 'profile_path': None}, {'credit_id': '58979fae925141730000017e', 'department': 'Directing', 'gender': 0, 'id': 1673599, 'job': 'Director', 'name': 'Tharun Bhascker Dhaassyam', 'profile_path': None}, {'credit_id': '584ad597c3a368141a021fb3', 'department': 'Writing', 'gender': 0, 'id': 1673599, 'job': 'Writer', 'name': 'Tharun Bhascker Dhaassyam', 'profile_path': None}, {'credit_id': '584acff7c3a368142801db85', 'department': 'Production', 'gender': 0, 'id': 1673602, 'job': 'Producer', 'name': 'Yash Rangineni', 'profile_path': None}, {'credit_id': '584acfe092514119bf01fee6', 'department': 'Production', 'gender': 0, 'id': 1720222, 'job': 'Producer', 'name': 'Raj Kandukuri', 'profile_path': None}]Tharun Bhascker Dhaassyam/name/nm5056902/Abhay Bethiganti, \nKeshav Deepak, \nVijay Deverakonda, \nAnish Kuruvilla/name/nm8232982/,/name/nm6642302/,/name/nm4797922/,/name/nm2378839/left_only
5#SELFIEPARTYNaNNaNNaN[{'id': 35, 'name': 'Comedy'}]90.0Four friends who are all students, wake up at the police station, not remembering the events of yesterday. They are accused of murdering a dwarf, whose body was found on the lawn of the house where the boys partied last night. To figure out what happened, the boys decide to collect all the photos and videos from yesterday's mayhem.NaNuk0.0#selfieparty[{'iso_3166_1': 'UA', 'name': 'Ukraine'}]tt5335198[]2016-03-312016.0[{'cast_id': 1, 'character': '', 'credit_id': '56ef3f31c3a368226000ce52', 'gender': 1, 'id': 1593567, 'name': 'Nadiya Lukavetska ', 'order': 1, 'profile_path': '/mMbFytMa1ByOmOgzE0xhjFufEM.jpg'}][{'credit_id': '56ef3c16c3a368225300ccb1', 'department': 'Directing', 'gender': 0, 'id': 1086533, 'job': 'Director', 'name': 'Lubomir Levitski', 'profile_path': None}]Lyubomyr Levytsky/name/nm2463500/Sergei Zorenko, \nBoris Savenko, \nKseniya Mishina, \nArthur Shurypa/name/nm7881765/,/name/nm7881762/,/name/nm2432008/,/name/nm7881761/left_only
6$50K and a Call Girl: A Love StoryNaNNaNNaN[{'id': 18, 'name': 'Drama'}]90.0When Ross is diagnosed with terminal brain cancer and given six weeks to live, his newly engaged older brother Seth offers to spend his $50,000 wedding fund on a final trip of a lifetime. Their plans are complicated when Ross invites a call girl to join the group and Seth’s uptight fiancée insists on tagging along. This raucous road trip comedy features hip-hop star Asher Roth.Ren6.3$50kandacallgirl:alovestory[{'iso_3166_1': 'US', 'name': 'United States of America'}]tt2106284[]2014-01-102014.0[{'cast_id': 2, 'character': 'Ross', 'credit_id': '52fe4f31c3a36847f82c42ad', 'gender': 2, 'id': 77809, 'name': 'Ross Patterson', 'order': 0, 'profile_path': '/3Qd6FIEjAoakyFpgm4e0wxrzvzP.jpg'}, {'cast_id': 3, 'character': 'Carly', 'credit_id': '52fe4f31c3a36847f82c42b1', 'gender': 1, 'id': 228276, 'name': 'Jessie Wiseman', 'order': 1, 'profile_path': '/r5Qaj5Epj1KULIOOVqoHoPiHL7I.jpg'}, {'cast_id': 4, 'character': 'Seth', 'credit_id': '52fe4f31c3a36847f82c42b5', 'gender': 2, 'id': 87438, 'name': 'Seth Grossman', 'order': 2, 'profile_path': None}, {'cast_id': 5, 'character': 'Lauren', 'credit_id': '52fe4f31c3a36847f82c42b9', 'gender': 0, 'id': 1286162, 'name': 'Lauren Aboulafia', 'order': 3, 'profile_path': None}, {'cast_id': 8, 'character': 'Asher', 'credit_id': '58414508c3a36865b300531a', 'gender': 0, 'id': 1717286, 'name': 'Asher Roth', 'order': 4, 'profile_path': None}, {'cast_id': 9, 'character': 'Penelope', 'credit_id': '584145159251417d500055fe', 'gender': 1, 'id': 210346, 'name': 'Keena Ferguson', 'order': 5, 'profile_path': '/l3Sf2uUwpFvPCvJfiBj9H3PiUmE.jpg'}, {'cast_id': 10, 'character': 'Jesse Winchester', 'credit_id': '5841452c9251417d620055df', 'gender': 2, 'id': 1717282, 'name': 'Al Carabello', 'order': 6, 'profile_path': '/33NyJLAxt2KOVQDxpmHL6Ytxmat.jpg'}][{'credit_id': '52fe4f31c3a36847f82c42c5', 'department': 'Writing', 'gender': 2, 'id': 77809, 'job': 'Writer', 'name': 'Ross Patterson', 'profile_path': '/3Qd6FIEjAoakyFpgm4e0wxrzvzP.jpg'}, {'credit_id': '52fe4f31c3a36847f82c42a9', 'department': 'Directing', 'gender': 2, 'id': 87438, 'job': 'Director', 'name': 'Seth Grossman', 'profile_path': None}, {'credit_id': '52fe4f31c3a36847f82c42bf', 'department': 'Writing', 'gender': 2, 'id': 87438, 'job': 'Writer', 'name': 'Seth Grossman', 'profile_path': None}]Seth Grossman/name/nm0343713/Ross Patterson, \nJessie Wiseman, \nSeth Grossman, \nLauren Aboulafia/name/nm0666388/,/name/nm3034435/,/name/nm0343713/,/name/nm2992430/left_only
7$5 a DayNaNNaNNaN[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name': 'Comedy'}]98.0After being released from jail, the son of a con man joins his father on the road.NaNen6.0$5aday[{'iso_3166_1': 'US', 'name': 'United States of America'}]tt1024733[{'name': 'New Line Cinema', 'id': 12}]2008-01-012008.0[{'cast_id': 13, 'character': 'Nat Parker', 'credit_id': '52fe43b2c3a36847f8068855', 'gender': 2, 'id': 4690, 'name': 'Christopher Walken', 'order': 0, 'profile_path': '/ysO1GwRzLT9OVAB9Y2SKHxomqDr.jpg'}, {'cast_id': 14, 'character': 'Ritchie Flynn Parker', 'credit_id': '52fe43b2c3a36847f8068859', 'gender': 2, 'id': 4941, 'name': 'Alessandro Nivola', 'order': 1, 'profile_path': '/lLlaotVNM6dhjXj7GWVLSedugct.jpg'}, {'cast_id': 12, 'character': 'Dolores Jones', 'credit_id': '52fe43b2c3a36847f8068851', 'gender': 1, 'id': 4430, 'name': 'Sharon Stone', 'order': 2, 'profile_path': '/m57fGJemBTpsjydGlaZJywlIWz4.jpg'}, {'cast_id': 15, 'character': 'Rick Carlson', 'credit_id': '52fe43b2c3a36847f806885d', 'gender': 2, 'id': 21721, 'name': 'Dean Cain', 'order': 3, 'profile_path': '/cICjOmo5KZWdfJLBvIExDE6Ma5f.jpg'}, {'cast_id': 16, 'character': 'Maggie', 'credit_id': '52fe43b2c3a36847f8068861', 'gender': 1, 'id': 2956, 'name': 'Amanda Peet', 'order': 4, 'profile_path': '/81KzrntoeEztNpfhCYNgfOtI3kU.jpg'}, {'cast_id': 9, 'character': 'Tim Webber', 'credit_id': '52fe43b2c3a36847f8068845', 'gender': 2, 'id': 36135, 'name': 'Christopher Dempsey', 'order': 5, 'profile_path': '/nOX8sCxl0LRFYMkYauWFD0xwaDm.jpg'}, {'cast_id': 10, 'character': 'Mrs. Carlson', 'credit_id': '52fe43b2c3a36847f8068849', 'gender': 0, 'id': 36136, 'name': 'Bridget Ann White', 'order': 6, 'profile_path': '/ysgN1bjaiC7Pwts12eF3jD8EmfS.jpg'}, {'cast_id': 11, 'character': 'Sherry', 'credit_id': '52fe43b2c3a36847f806884d', 'gender': 1, 'id': 36137, 'name': 'Nectar Rose', 'order': 7, 'profile_path': '/p5DyM7147ELNpuBXIUbz0LteBuQ.jpg'}, {'cast_id': 17, 'character': '', 'credit_id': '52fe43b2c3a36847f8068865', 'gender': 2, 'id': 9979, 'name': 'Peter Coyote', 'order': 8, 'profile_path': '/5zVvYZxE6T0OeL1iaFBBCzY3QOi.jpg'}][{'credit_id': '52fe43b2c3a36847f8068835', 'department': 'Production', 'gender': 2, 'id': 1593, 'job': 'Casting', 'name': 'Joseph Middleton', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f8068817', 'department': 'Directing', 'gender': 2, 'id': 24248, 'job': 'Director', 'name': 'Nigel Cole', 'profile_path': '/2CIcwfX3zzzTYqUPUQzKdnUgQl7.jpg'}, {'credit_id': '52fe43b2c3a36847f806881d', 'department': 'Writing', 'gender': 0, 'id': 36129, 'job': 'Screenplay', 'name': 'Neal H. Dobrofsky', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f8068823', 'department': 'Writing', 'gender': 0, 'id': 36130, 'job': 'Screenplay', 'name': 'Tippi Dobrofsky', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f8068829', 'department': 'Production', 'gender': 1, 'id': 36131, 'job': 'Producer', 'name': 'Carol Baum', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f806882f', 'department': 'Production', 'gender': 1, 'id': 36132, 'job': 'Producer', 'name': 'Jane Goldenring', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f806883b', 'department': 'Art', 'gender': 0, 'id': 36133, 'job': 'Production Design', 'name': 'Bryce Perrin', 'profile_path': None}, {'credit_id': '52fe43b2c3a36847f8068841', 'department': 'Production', 'gender': 0, 'id': 36134, 'job': 'Producer', 'name': 'Kia Jam', 'profile_path': None}]NaNNaNNaNNaNleft_only
8$9.99NaNNaN52107.0[{'id': 16, 'name': 'Animation'}, {'id': 18, 'name': 'Drama'}]78.0Have you ever wondered "What is the meaning of life? Why do we exist?" The answer to this vexing question is now within your reach! You'll find it in a small yet amazing booklet, which will explain, in easy to follow, simple terms your reason for being! The booklet, printed on the finest paper, contains illuminating, exquisite colour pictures, and could be yours for a mere $9.99.Ren6.0$9.99[{'iso_3166_1': 'AU', 'name': 'Australia'}, {'iso_3166_1': 'IL', 'name': 'Israel'}]tt0790799[{'name': 'Australian Film Finance Corporation', 'id': 1380}]2008-09-042008.0[{'cast_id': 4, 'character': 'Bisley (voice)', 'credit_id': '52fe47cf9251416c750a6281', 'gender': 2, 'id': 37289, 'name': 'Tom Budge', 'order': 0, 'profile_path': '/yu5L2Rw36Y6to5YMcjI3JXMfIBH.jpg'}, {'cast_id': 5, 'character': 'n/a', 'credit_id': '52fe47cf9251416c750a6285', 'gender': 0, 'id': 84450, 'name': 'Josef Ber', 'order': 1, 'profile_path': '/4pKNxJpfr6Ow5adomvHSIkBg4qt.jpg'}, {'cast_id': 6, 'character': 'Ron (voice)', 'credit_id': '52fe47cf9251416c750a6289', 'gender': 2, 'id': 33192, 'name': 'Joel Edgerton', 'order': 2, 'profile_path': '/lkOkaMKSRRGLMgkLaCzR9sYgTgx.jpg'}, {'cast_id': 7, 'character': 'Stanton (voice)', 'credit_id': '52fe47cf9251416c750a628d', 'gender': 2, 'id': 84451, 'name': 'Leon Ford', 'order': 3, 'profile_path': None}, {'cast_id': 8, 'character': 'Dave Peck (voice)', 'credit_id': '52fe47cf9251416c750a6291', 'gender': 2, 'id': 77138, 'name': 'Samuel Johnson', 'order': 4, 'profile_path': '/8X6DZna6wS9jx4Gext3QNtbtcPH.jpg'}, {'cast_id': 9, 'character': 'Michelle (voice)', 'credit_id': '52fe47cf9251416c750a6295', 'gender': 1, 'id': 79966, 'name': 'Claudia Karvan', 'order': 5, 'profile_path': '/4M0Z25W9hI2ihSa6xBrdFxmBdTD.jpg'}, {'cast_id': 10, 'character': 'Zack (voice)', 'credit_id': '52fe47cf9251416c750a6299', 'gender': 2, 'id': 84452, 'name': 'Jamie Katsamatsas', 'order': 6, 'profile_path': None}, {'cast_id': 11, 'character': 'Jim Peck (voice)', 'credit_id': '52fe47cf9251416c750a629d', 'gender': 2, 'id': 57829, 'name': 'Anthony LaPaglia', 'order': 7, 'profile_path': '/22sOEc37TsxxcnIl1XmucuW3IDl.jpg'}, {'cast_id': 12, 'character': 'Lenny Peck (voice)', 'credit_id': '52fe47cf9251416c750a62a1', 'gender': 2, 'id': 77335, 'name': 'Ben Mendelsohn', 'order': 8, 'profile_path': '/nAeZkSUXh9CUAUq1cFAg77rZLIS.jpg'}, {'cast_id': 13, 'character': 'Angel (voice)', 'credit_id': '52fe47cf9251416c750a62a5', 'gender': 2, 'id': 118, 'name': 'Geoffrey Rush', 'order': 9, 'profile_path': '/5h91WHSK80YtqTk1bMiar2IZzO2.jpg'}][{'credit_id': '52fe47cf9251416c750a627d', 'department': 'Writing', 'gender': 0, 'id': 55046, 'job': 'Screenplay', 'name': 'Etgar Keret', 'profile_path': None}, {'credit_id': '5734e7c09251417b3e00059f', 'department': 'Writing', 'gender': 1, 'id': 84448, 'job': 'Screenplay', 'name': 'Tatia Rosenthal', 'profile_path': None}, {'credit_id': '52fe47cf9251416c750a6271', 'department': 'Directing', 'gender': 1, 'id': 84448, 'job': 'Director', 'name': 'Tatia Rosenthal', 'profile_path': None}, {'credit_id': '552eb7f1c3a36804cd0019a9', 'department': 'Visual Effects', 'gender': 0, 'id': 1448052, 'job': 'Animation', 'name': 'Anthony Elworthy', 'profile_path': None}, {'credit_id': '552eb7b9c3a368750100465a', 'department': 'Visual Effects', 'gender': 0, 'id': 1453480, 'job': 'Animation', 'name': 'Daniel Alderson', 'profile_path': None}, {'credit_id': '552eb7fbc3a3687501004669', 'department': 'Visual Effects', 'gender': 0, 'id': 1453527, 'job': 'Animation', 'name': 'Jan-Erik Maas', 'profile_path': None}, {'credit_id': '552eb80ec3a368413100305e', 'department': 'Visual Effects', 'gender': 0, 'id': 1453649, 'job': 'Animation', 'name': 'Andy Spilsted', 'profile_path': None}, {'credit_id': '552eb7c4c3a3686be2003960', 'department': 'Visual Effects', 'gender': 0, 'id': 1454649, 'job': 'Animation', 'name': 'Yonatan Bereskin', 'profile_path': None}, {'credit_id': '552eb7d0c3a3686c4e0037a3', 'department': 'Visual Effects', 'gender': 0, 'id': 1454650, 'job': 'Animation', 'name': 'Darren Burgess', 'profile_path': None}, {'credit_id': '552eb7e7c3a36804cd0019a0', 'department': 'Visual Effects', 'gender': 0, 'id': 1454651, 'job': 'Animation', 'name': 'Steve Cox', 'profile_path': None}, {'credit_id': '552eb80592514103ce007dd9', 'department': 'Visual Effects', 'gender': 0, 'id': 1454652, 'job': 'Animation', 'name': 'Sharon Parker', 'profile_path': None}, {'credit_id': '552eb8199251413b3b000fd2', 'department': 'Visual Effects', 'gender': 0, 'id': 1454653, 'job': 'Animation', 'name': 'Norman Yeend', 'profile_path': None}]Tatia Rosenthal/name/nm1038127/Geoffrey Rush, \nAnthony LaPaglia, \nSamuel Johnson, \nBarry Otto/name/nm0001691/,/name/nm0001439/,/name/nm0426170/,/name/nm0653222/left_only
9$ DollarsNaNNaN4400000.0[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name': 'Crime'}, {'id': 35, 'name': 'Comedy'}]121.0A bank security expert plots with a call girl to rob the safety deposit boxes of three very different criminals from a high-tech bank in Hamburg.Ren6.6$dollars[{'iso_3166_1': 'US', 'name': 'United States of America'}]tt0068152[{'name': 'Columbia Pictures Corporation', 'id': 441}, {'name': 'Worldwide', 'id': 55873}, {'name': 'Pan', 'id': 55874}]1971-12-171971.0[{'cast_id': 1, 'character': 'Joe Collins', 'credit_id': '52fe44929251416c910155cb', 'gender': 2, 'id': 6449, 'name': 'Warren Beatty', 'order': 0, 'profile_path': '/1fsYRe1WhIohbfk8qHmjMcuCXeJ.jpg'}, {'cast_id': 2, 'character': 'Dawn Divine', 'credit_id': '52fe44929251416c910155cf', 'gender': 1, 'id': 18892, 'name': 'Goldie Hawn', 'order': 1, 'profile_path': '/6uCoDnRMwk9zGUYnuI2VPvDUjU6.jpg'}, {'cast_id': 3, 'character': 'Mr. Kessel', 'credit_id': '52fe44929251416c910155d3', 'gender': 2, 'id': 9908, 'name': 'Gert Fröbe', 'order': 2, 'profile_path': '/eMXyEZXTWAcfUXPACYfG0pka0Id.jpg'}, {'cast_id': 4, 'character': 'Attorney', 'credit_id': '52fe44929251416c910155d7', 'gender': 2, 'id': 5255, 'name': 'Robert Webber', 'order': 3, 'profile_path': '/mg6SHDi5bi9C7pUwH14ZXxTnR7M.jpg'}, {'cast_id': 5, 'character': 'Sarge', 'credit_id': '52fe44929251416c910155db', 'gender': 2, 'id': 14847, 'name': 'Scott Brady', 'order': 4, 'profile_path': '/2JlQcMIbISvcQjtO4iIB1yZSqwW.jpg'}, {'cast_id': 8, 'character': 'Candy Man', 'credit_id': '54dd58dec3a368454d000827', 'gender': 2, 'id': 22623, 'name': 'Arthur Brauss', 'order': 5, 'profile_path': '/tSMGcGiM0BCZqVPEHfrPVF4rU4d.jpg'}, {'cast_id': 9, 'character': 'Major', 'credit_id': '54dd58ef925141195000081e', 'gender': 0, 'id': 1426629, 'name': 'Robert Stiles', 'order': 6, 'profile_path': None}, {'cast_id': 10, 'character': 'Granich', 'credit_id': '54dd5904c3a36854460002f5', 'gender': 2, 'id': 26517, 'name': 'Wolfgang Kieling', 'order': 7, 'profile_path': '/7kkIf5WtM3ickLQglG9PoqeWn2e.jpg'}, {'cast_id': 16, 'character': 'Helga', 'credit_id': '58b602d8925141260a001031', 'gender': 1, 'id': 40974, 'name': 'Christiane Maybach', 'order': 8, 'profile_path': '/bBsjTvhSjQdhiKwVfkdD3CeVDEA.jpg'}, {'cast_id': 17, 'character': 'Bodyguard (as Robert Herron)', 'credit_id': '58b61c6a92514122e0002376', 'gender': 0, 'id': 1016007, 'name': 'Bob Herron', 'order': 9, 'profile_path': None}, {'cast_id': 18, 'character': 'Karl', 'credit_id': '58b61c8d9251412ab1001f09', 'gender': 0, 'id': 1673380, 'name': 'Hans Hutter', 'order': 10, 'profile_path': None}, {'cast_id': 19, 'character': 'Berta', 'credit_id': '58b61cb79251416129004b8d', 'gender': 0, 'id': 1767429, 'name': 'Monica Stender', 'order': 11, 'profile_path': None}, {'cast_id': 20, 'character': 'Bruno', 'credit_id': '58b61ccfc3a3687be6002630', 'gender': 2, 'id': 26334, 'name': 'Horst Hesslein', 'order': 12, 'profile_path': None}, {'cast_id': 21, 'character': 'Furcoat', 'credit_id': '58b61d00c3a36806780021f4', 'gender': 0, 'id': 1171585, 'name': 'Wolfgang Kuhlmann', 'order': 13, 'profile_path': None}, {'cast_id': 22, 'character': 'Knifeman (as Klaus Tschichan)', 'credit_id': '58b61d25925141260a0020e6', 'gender': 0, 'id': 1350708, 'name': 'Klaus Schichan', 'order': 14, 'profile_path': None}][{'credit_id': '52fe44929251416c910155e1', 'department': 'Directing', 'gender': 2, 'id': 3632, 'job': 'Director', 'name': 'Richard Brooks', 'profile_path': '/2xTXd8iz1kKrH0sV4KFuEwZs62O.jpg'}, {'credit_id': '52fe44929251416c910155e7', 'department': 'Writing', 'gender': 2, 'id': 3632, 'job': 'Writer', 'name': 'Richard Brooks', 'profile_path': '/2xTXd8iz1kKrH0sV4KFuEwZs62O.jpg'}, {'credit_id': '54dd594fc3a3684558000844', 'department': 'Sound', 'gender': 2, 'id': 13301, 'job': 'Music', 'name': 'Quincy Jones', 'profile_path': '/mV7hXHeUSXV7KZEfsxJ8lMrKpAt.jpg'}, {'credit_id': '572add60c3a36848010013c4', 'department': 'Camera', 'gender': 2, 'id': 45717, 'job': 'Director of Photography', 'name': 'Petrus R. Schlömp', 'profile_path': None}, {'credit_id': '54dd598a925141194b000888', 'department': 'Editing', 'gender': 0, 'id': 67894, 'job': 'Editor', 'name': 'George Grenville', 'profile_path': None}, {'credit_id': '54dd596a92514103bb000354', 'department': 'Production', 'gender': 0, 'id': 1293412, 'job': 'Producer', 'name': 'M.J. Frankovich', 'profile_path': '/72g1p035ipyZaV3NglZFgtRkaey.jpg'}, {'credit_id': '58b61e319251411d14002a52', 'department': 'Production', 'gender': 0, 'id': 1467196, 'job': 'Unit Manager', 'name': 'Frank Winterstein', 'profile_path': None}, {'credit_id': '58b61e0792514122e00024a8', 'department': 'Sound', 'gender': 0, 'id': 1767430, 'job': 'Music Editor', 'name': 'Ralph Hall', 'profile_path': None}]Richard Brooks/name/nm0112218/Warren Beatty, \nGoldie Hawn, \nGert Fröbe, \nRobert Webber/name/nm0000886/,/name/nm0000443/,/name/nm0002085/,/name/nm0916434/left_only
final_titlefinal_budgetfinal_worldwide_boxofficefinal_domestic_boxofficefinal_genresfinal_runtimefinal_overviewfinal_certificatefinal_original_languagefinal_ratingfinal_clean_titlefinal_production_countriesimdb_idproduction_companiesrelease_datefinal_yearcastcrewdirectordirector_idstarstar_id_merge
49856후궁: 제왕의 첩NaNNaNNaN[{'id': 18, 'name': 'Drama'}]122.0Set during the early Joseon Dynasty, the film begins with the queen mother and former concubine (Park Ji-young) in a precarious position of having no blood ties to the childless king (Jung Chan). She schemes to replace him on the throne with his stepbrother and her submissive young son Seong-won (Kim Dong-wook). Indifferent to his mother’s plans, the timid prince falls in love at first sight with Hwa-yeon (Jo Yeo-jeong), an aristocrat’s daughter, who has already found love with Kwon-yoo (Kim Min-joon), a commoner. The king is eventually poisoned to death by the queen mother, who is desperate to be in power. Hwa-yeon is moved to a closely watched humble residence, with the queen mother planning to assassinate Hwa-yeon and her son to secure her position in the palace.Unratedko5.9후궁:제왕의첩[{'iso_3166_1': 'KR', 'name': 'South Korea'}]tt2544120[{'name': 'Lotte Entertainment', 'id': 7819}]2012-06-062012.0[{'cast_id': 28, 'character': 'Hwa-Yeon', 'credit_id': '530df561c3a3685bfb00285f', 'gender': 1, 'id': 556435, 'name': 'Jo Yeo-jeong', 'order': 0, 'profile_path': '/e1a4KlwRzp1jsHxVYjiLtVdrDH9.jpg'}, {'cast_id': 29, 'character': 'Grand Prince Seong-Won', 'credit_id': '530df5ccc3a3685c12002939', 'gender': 2, 'id': 118976, 'name': 'Kim Dong-wook', 'order': 1, 'profile_path': '/mXOe140fwEAJkAkLyWldv8Hs7ux.jpg'}, {'cast_id': 37, 'character': 'Kwon-yoo / Choong-young', 'credit_id': '55ccd662c3a3681bf20001c4', 'gender': 2, 'id': 1257594, 'name': 'Kim Min-joon', 'order': 2, 'profile_path': '/xQd1tqNbLiyJtZF9I8O6KWL9FjR.jpg'}, {'cast_id': 40, 'character': "King's mother", 'credit_id': '57c9b79b9251413de30002f2', 'gender': 1, 'id': 1673497, 'name': 'Park Ji-young', 'order': 3, 'profile_path': '/wrXitjj4YZlUdgqOXXbnqhHKxnw.jpg'}, {'cast_id': 33, 'character': 'Geum-Ok', 'credit_id': '554dadd19251413e4000181a', 'gender': 0, 'id': 1080933, 'name': 'Jo Eun-Ji', 'order': 4, 'profile_path': '/hy0sKZFquiggTmCPmCa9yjPHCpK.jpg'}, {'cast_id': 39, 'character': 'Chief eunuch', 'credit_id': '57b1a2f79251410e95001762', 'gender': 2, 'id': 1293080, 'name': 'Lee Kyoung-young', 'order': 5, 'profile_path': '/aJT9impAPJwixrvL7EjRhtgd4v0.jpg'}, {'cast_id': 36, 'character': "Hwayeon's father", 'credit_id': '55be0594c3a36865260041d9', 'gender': 0, 'id': 1239677, 'name': 'Ahn Suk-hwan', 'order': 6, 'profile_path': '/wms1Fpki825XbOM8drFx9n5NpAv.jpg'}][{'credit_id': '53401d410e0a2679ad00009d', 'department': 'Crew', 'gender': 0, 'id': 1299478, 'job': 'Lighting Camera', 'name': 'Daehui Kang', 'profile_path': None}, {'credit_id': '562d4da8c3a3681b4b008174', 'department': 'Writing', 'gender': 0, 'id': 232409, 'job': 'Screenplay', 'name': 'Mee-jeung Kim', 'profile_path': '/zrpfwOOijjvzDGa0A0onzIcKBd2.jpg'}, {'credit_id': '53038eed925141219b5b14e8', 'department': 'Writing', 'gender': 0, 'id': 1294139, 'job': 'Screenplay', 'name': 'Yunjeong Hwang', 'profile_path': None}, {'credit_id': '53038ef992514161a232ad0d', 'department': 'Production', 'gender': 0, 'id': 1294140, 'job': 'Producer', 'name': 'Park Seongil', 'profile_path': None}, {'credit_id': '53038f05925141218f5ff951', 'department': 'Crew', 'gender': 0, 'id': 1294141, 'job': 'Cinematography', 'name': 'Giseok Hwang', 'profile_path': None}, {'credit_id': '53038f2492514121925e765d', 'department': 'Sound', 'gender': 0, 'id': 1293612, 'job': 'Original Music Composer', 'name': 'Yeonguk Jo', 'profile_path': None}, {'credit_id': '53038f3292514121a4622605', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1293613, 'job': 'Costume Design', 'name': 'Sanggyeong Jo', 'profile_path': None}, {'credit_id': '53038f3b92514121955b4855', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1294144, 'job': 'Costume Design', 'name': 'Mira Yun', 'profile_path': None}, {'credit_id': '5551151fc3a3687f63007188', 'department': 'Directing', 'gender': 2, 'id': 132177, 'job': 'Director', 'name': 'Kim Dae-seung', 'profile_path': '/upTNQBHwfy9xGaOKKqDrCBTxLfA.jpg'}, {'credit_id': '5551152ac3a368436d001091', 'department': 'Writing', 'gender': 2, 'id': 132177, 'job': 'Screenplay', 'name': 'Kim Dae-seung', 'profile_path': '/upTNQBHwfy9xGaOKKqDrCBTxLfA.jpg'}]Dae-seung Kim/name/nm0453413/Cho Yeo-jeong, \nDong-wook Kim, \nMin-Joon Kim, \nPark Ji-young/name/nm1856097/,/name/nm1118612/,/name/nm2024084/,/name/nm4735633/left_only
49857후회하지 않아NaNNaN14620.0[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]113.0Sumin is an orphan trying to balance work in a factory with study at an art college and an evening job. One night, a rich young businessman makes an advance on him during one of his driving jobs. They meet again the next day: it is during a round of redundancy cuts at the factory where Sumin refuses an attempt by a man (who is in fact the boss' son Jaemin) to save his job. Eventually, Sumin is seduced into working as a prostitute in an up-market boy-brothel as Jaemin's obsession with him grows, leaving Jaemin helpless in the face of his overwhelming desire. This is a film of bold sexuality, where unexpected passion, desire and misunderstandings wreak havoc of an operatic intensity.Rko6.4후회하지않아[{'iso_3166_1': 'KR', 'name': 'South Korea'}]tt0996948[{'name': 'Generation Blue Films', 'id': 79409}]2006-11-162006.0[{'cast_id': 4, 'character': 'Song Jae-min', 'credit_id': '578c40879251410824001372', 'gender': 2, 'id': 110388, 'name': 'Kim Nam-gil', 'order': 2, 'profile_path': '/beafwFkUeyXCakywsTNpVb0b4hm.jpg'}, {'cast_id': 5, 'character': 'Lee Su-min', 'credit_id': '578c40c39251417aca00ceff', 'gender': 2, 'id': 1652535, 'name': 'Lee Young-Hoon', 'order': 3, 'profile_path': '/oq7ITPpmCd5TV8r8jvXCknk7oMz.jpg'}, {'cast_id': 6, 'character': 'Ga-ram', 'credit_id': '58385cf79251414cb9016a5f', 'gender': 2, 'id': 118976, 'name': 'Kim Dong-wook', 'order': 4, 'profile_path': '/mXOe140fwEAJkAkLyWldv8Hs7ux.jpg'}, {'cast_id': 8, 'character': 'Madame', 'credit_id': '59154c90c3a3683a9301847b', 'gender': 2, 'id': 1420814, 'name': 'Jung Seung-kil', 'order': 5, 'profile_path': None}][{'credit_id': '59154ca0925141583c019146', 'department': 'Directing', 'gender': 2, 'id': 1030914, 'job': 'Director', 'name': 'Leesong Hee-il', 'profile_path': '/hY5C9atZTM9jIdnpv0yWuJ3ny7p.jpg'}, {'credit_id': '59154caac3a3683a93018491', 'department': 'Writing', 'gender': 2, 'id': 1030914, 'job': 'Writer', 'name': 'Leesong Hee-il', 'profile_path': '/hY5C9atZTM9jIdnpv0yWuJ3ny7p.jpg'}]Hee-il Leesong/name/nm1276328/Nam-gil Kim, \nYoung-hoon Lee, \nHyeon-cheol Jo, \nDong-wook Kim/name/nm2339825/,/name/nm2608040/,/name/nm2604957/,/name/nm1118612/left_only
49858龍在江湖NaNNaNNaN[{'id': 10769, 'name': 'Foreign'}]112.0A low level gangster in Hong Kong gains new respect after saving a boss's life in a gang fight. Despite his wife's death in this attack, he appears to be moving up in the Triad family until he discovers the other bosses are not looking out for him when he is double crossed and the gangster he previously hospitalized in the gang fight comes back for revenge.NaNcn7.0龍在江湖[]tt0165998[]1998-07-011998.0[{'cast_id': 17, 'character': 'Wai Cheung-Dee', 'credit_id': '52fe4961c3a36847f81971e7', 'gender': 2, 'id': 25246, 'name': 'Andy Lau', 'order': 0, 'profile_path': '/8LCbwZJhjJRew59dJ7xSqyozVWf.jpg'}, {'cast_id': 3, 'character': 'Sandy Leung', 'credit_id': '52fe4961c3a36847f81971af', 'gender': 0, 'id': 65938, 'name': 'Gigi Leung', 'order': 1, 'profile_path': '/sx6eq3Zpi1FyEGW8IAWWKVi4vFg.jpg'}, {'cast_id': 4, 'character': 'Ruby Kwan', 'credit_id': '52fe4961c3a36847f81971b3', 'gender': 1, 'id': 227804, 'name': 'Suki Kwan', 'order': 2, 'profile_path': '/b8suIKTFSxLm4wDdYyXUXUrNqg8.jpg'}, {'cast_id': 5, 'character': 'Prince', 'credit_id': '52fe4961c3a36847f81971b7', 'gender': 2, 'id': 65197, 'name': 'Mark Cheng', 'order': 3, 'profile_path': '/dOX91UKJe1ZazravuzjBSpQG82k.jpg'}, {'cast_id': 6, 'character': 'Michael', 'credit_id': '52fe4961c3a36847f81971bb', 'gender': 2, 'id': 21914, 'name': 'Alex Fong Chung-Sun', 'order': 4, 'profile_path': '/lqSnU4hYkr8VyNnG03mMrmB9dwl.jpg'}, {'cast_id': 7, 'character': 'David', 'credit_id': '52fe4961c3a36847f81971bf', 'gender': 0, 'id': 227805, 'name': 'Joe Ma', 'order': 5, 'profile_path': '/ct2ZMpYGSeyWxsVfb3RIpNDC8u1.jpg'}, {'cast_id': 8, 'character': 'Skinny', 'credit_id': '52fe4961c3a36847f81971c3', 'gender': 0, 'id': 74193, 'name': 'Sam Lee', 'order': 6, 'profile_path': '/peBEATyBMxLnbqdg9DLBZVyJXkY.jpg'}, {'cast_id': 9, 'character': 'Fai', 'credit_id': '52fe4961c3a36847f81971c7', 'gender': 0, 'id': 65943, 'name': 'Chi Hung Ng', 'order': 7, 'profile_path': None}, {'cast_id': 10, 'character': 'Crazy Ball', 'credit_id': '52fe4961c3a36847f81971cb', 'gender': 0, 'id': 227806, 'name': 'Ben Ng', 'order': 8, 'profile_path': '/x62jhwD0uue1jhxX02Q7PbS7gOm.jpg'}, {'cast_id': 11, 'character': 'Uncle Hei', 'credit_id': '52fe4961c3a36847f81971cf', 'gender': 2, 'id': 83358, 'name': 'Michael Chan Wai-Man', 'order': 9, 'profile_path': '/pApINF6vLdiufL5T73yeYkd6jHv.jpg'}, {'cast_id': 12, 'character': 'Uncle Pao', 'credit_id': '52fe4961c3a36847f81971d3', 'gender': 2, 'id': 122816, 'name': 'Lee Siu-Kei', 'order': 10, 'profile_path': '/yEZS8hArr57X9as49TkxGaJXeW8.jpg'}, {'cast_id': 13, 'character': 'Uncle Fa', 'credit_id': '52fe4961c3a36847f81971d7', 'gender': 0, 'id': 112988, 'name': 'Tian-lin Wang', 'order': 11, 'profile_path': None}, {'cast_id': 14, 'character': 'Cindy', 'credit_id': '52fe4961c3a36847f81971db', 'gender': 0, 'id': 227807, 'name': 'Angie Cheung', 'order': 12, 'profile_path': '/5iAGeVHd3DpXExhycBle0wdDu9a.jpg'}, {'cast_id': 15, 'character': 'Judge', 'credit_id': '52fe4961c3a36847f81971df', 'gender': 2, 'id': 64662, 'name': 'Dennis Chan', 'order': 13, 'profile_path': '/qZiaEH1JJt8c0WUPJl9gqArGT2P.jpg'}, {'cast_id': 16, 'character': 'Fifi', 'credit_id': '52fe4961c3a36847f81971e3', 'gender': 0, 'id': 227808, 'name': 'Danise Chan', 'order': 14, 'profile_path': None}][{'credit_id': '52fe4961c3a36847f81971ed', 'department': 'Directing', 'gender': 2, 'id': 548474, 'job': 'Director', 'name': 'Wong Jing', 'profile_path': '/asZClP70V7W8zlFDzAzuQFGBZtC.jpg'}]Jing Wong/name/nm0939147/Andy Lau, \nGigi Leung, \nSuki Kwan, \nMark Cheng/name/nm0490489/,/name/nm0504942/,/name/nm0477105/,/name/nm0155599/left_only
49859琉璃樽NaNNaNNaN[{'id': 12, 'name': 'Adventure'}, {'id': 28, 'name': 'Action'}, {'id': 35, 'name': 'Comedy'}, {'id': 10749, 'name': 'Romance'}]121.0When Ah Bu, a girl from a small fishing town in Taiwan, finds a glass bottle with a romantic message, she travels to Hong Kong to find her prince charming. As it turns out, her prince charming, Albert, happens to be gay. But all is not lost when Ah Bu meets the dashing Chi Wu. Meanwhile, Ah Bu's boyfriend from Taiwan comes looking for her, as the action and romance follow Ah Bu back to Taiwan.PG-13cn6.0琉璃樽[{'iso_3166_1': 'HK', 'name': 'Hong Kong'}]tt0184526[{'name': 'Golden Harvest Pictures', 'id': 969}]1999-02-121999.0[{'cast_id': 1, 'character': 'C.N. Chan', 'credit_id': '52fe43d89251416c75020493', 'gender': 2, 'id': 18897, 'name': 'Jackie Chan', 'order': 0, 'profile_path': '/tEJazyboCJcsvxnhKH3Hf33Bmgj.jpg'}, {'cast_id': 2, 'character': 'Bu', 'credit_id': '52fe43d89251416c75020497', 'gender': 1, 'id': 21911, 'name': 'Shu Qi', 'order': 1, 'profile_path': '/kmTErFq6lKQww2Yk9AfpR2Q5YWx.jpg'}, {'cast_id': 3, 'character': 'Albert', 'credit_id': '52fe43d89251416c7502049b', 'gender': 2, 'id': 1337, 'name': 'Tony Leung Chiu-Wai', 'order': 2, 'profile_path': '/xSEwMBLHdiKjyG4YVCsjpaT5fgD.jpg'}, {'cast_id': 14, 'character': 'L.W. Lo', 'credit_id': '541da7f90e0a2612460093c6', 'gender': 0, 'id': 931174, 'name': 'Emil Chow', 'order': 3, 'profile_path': '/iB3clq9RFG4odnyBXG497Gv7bWZ.jpg'}, {'cast_id': 19, 'character': 'Alan', 'credit_id': '54dbbe8ac3a3682f2400207c', 'gender': 0, 'id': 134686, 'name': 'Bradley James Allan', 'order': 4, 'profile_path': '/pSOkpEcVF6sXnXMPQaws5j7AZpO.jpg'}, {'cast_id': 20, 'character': 'Long-Yi', 'credit_id': '54dbbea4925141619b00213e', 'gender': 2, 'id': 64496, 'name': 'Richie Ren', 'order': 5, 'profile_path': '/7hiyjKWSfTh8GplXX9mqgl5F24U.jpg'}, {'cast_id': 21, 'character': "Lo's Bodyguard", 'credit_id': '54dbbeb5c3a3681237002334', 'gender': 2, 'id': 70690, 'name': 'Ken Lo', 'order': 6, 'profile_path': '/3cxY30w6S6hraBUpCtY7S8PQhDq.jpg'}, {'cast_id': 24, 'character': "Bu's father", 'credit_id': '56f7b9b3c3a3686a5c0064a0', 'gender': 0, 'id': 974461, 'name': 'Chan Chung-Yung', 'order': 7, 'profile_path': '/wkpFVREWECzqKSmfN5KVgqt4A7j.jpg'}, {'cast_id': 23, 'character': "Bu's Mother", 'credit_id': '54dbbecb925141619e0023e0', 'gender': 0, 'id': 103598, 'name': 'Elaine Jin', 'order': 8, 'profile_path': '/vxJzPWCcRQ2nLiHfZmYwPTS3KEr.jpg'}, {'cast_id': 15, 'character': 'Pretty girl at seaside', 'credit_id': '548b4a43c3a36856e80003f8', 'gender': 0, 'id': 105421, 'name': 'Jo Kuk', 'order': 9, 'profile_path': '/2RN8Ubr1UeenZKDqg1uIBUdqixG.jpg'}, {'cast_id': 16, 'character': 'Airport Pickpocket', 'credit_id': '548b4dc59251416eb200049a', 'gender': 1, 'id': 88495, 'name': 'Sandra Ng Kwun-Yu', 'order': 10, 'profile_path': '/hIQWKC82xAQHvO5Cm4J31ZZLEEh.jpg'}, {'cast_id': 17, 'character': 'Xiao Jun', 'credit_id': '54a4e2089251414e2800e48c', 'gender': 0, 'id': 120419, 'name': 'Annie Wu', 'order': 11, 'profile_path': '/qcqEz2KHWUpjFJnO12fMidSFIU4.jpg'}, {'cast_id': 25, 'character': "Howie's bodyguard", 'credit_id': '573f0355c3a36839df0021a2', 'gender': 0, 'id': 1123830, 'name': 'William Chan Yuk-Choh', 'order': 12, 'profile_path': None}, {'cast_id': 26, 'character': 'Michele', 'credit_id': '576a0ab79251416e43001134', 'gender': 0, 'id': 1639507, 'name': 'Jacqueline Li', 'order': 13, 'profile_path': None}, {'cast_id': 27, 'character': 'Elaine', 'credit_id': '576a0ac1c3a36829a900125e', 'gender': 0, 'id': 987139, 'name': 'Eileen Tung', 'order': 14, 'profile_path': '/8Lvsv6C90LHYrYDLJ613seT1RMf.jpg'}, {'cast_id': 28, 'character': 'Policeman with dog', 'credit_id': '591041f39251414e8902fdcf', 'gender': 2, 'id': 57607, 'name': 'Stephen Chow', 'order': 15, 'profile_path': '/aizTZF5vHoOg6Ck7Nq5X65ULYoa.jpg'}][{'credit_id': '52fe43d89251416c750204c3', 'department': 'Writing', 'gender': 2, 'id': 18897, 'job': 'Screenplay', 'name': 'Jackie Chan', 'profile_path': '/tEJazyboCJcsvxnhKH3Hf33Bmgj.jpg'}, {'credit_id': '52fe43d89251416c750204b1', 'department': 'Production', 'gender': 2, 'id': 18897, 'job': 'Producer', 'name': 'Jackie Chan', 'profile_path': '/tEJazyboCJcsvxnhKH3Hf33Bmgj.jpg'}, {'credit_id': '52fe43d89251416c750204ab', 'department': 'Production', 'gender': 2, 'id': 46323, 'job': 'Producer', 'name': 'Raymond Chow', 'profile_path': '/zAIthSX6xZoaj1J09Uv6JzagKWP.jpg'}, {'credit_id': '52fe43d89251416c750204d5', 'department': 'Editing', 'gender': 2, 'id': 63574, 'job': 'Editor', 'name': 'Cheung Ka-Fai', 'profile_path': None}, {'credit_id': '52fe43d89251416c750204b7', 'department': 'Writing', 'gender': 0, 'id': 63577, 'job': 'Author', 'name': 'Yiu Fai Lo', 'profile_path': None}, {'credit_id': '52fe43d89251416c750204bd', 'department': 'Writing', 'gender': 2, 'id': 65934, 'job': 'Screenplay', 'name': 'Vincent Kok', 'profile_path': '/mTtFWZKHYSKNaS7GHSuVho4THp.jpg'}, {'credit_id': '52fe43d89251416c750204a5', 'department': 'Directing', 'gender': 2, 'id': 65934, 'job': 'Director', 'name': 'Vincent Kok', 'profile_path': '/mTtFWZKHYSKNaS7GHSuVho4THp.jpg'}, {'credit_id': '52fe43d89251416c750204c9', 'department': 'Sound', 'gender': 0, 'id': 67890, 'job': 'Music', 'name': 'Dang-Yi Wong', 'profile_path': None}, {'credit_id': '52fe43d89251416c750204cf', 'department': 'Camera', 'gender': 0, 'id': 67891, 'job': 'Director of Photography', 'name': 'Man Po Cheung', 'profile_path': None}]Vincent Kok/name/nm0463674/Jackie Chan, \nShu Qi, \nTony Chiu-Wai Leung, \nEmil Chau/name/nm0000329/,/name/nm0795517/,/name/nm0504897/,/name/nm0154235/left_only
498600課の女 赤い手錠NaNNaNNaN[{'id': 53, 'name': 'Thriller'}, {'id': 28, 'name': 'Action'}, {'id': 80, 'name': 'Crime'}, {'id': 18, 'name': 'Drama'}]88.0Agent Zero (Miki Sugimoto) is a cop that uses her own methods for dealing with criminals. After she unlawfully kills a rapist in a violent fashion, she is sent to prison and stripped of her badge. But very soon after, a rich politician's daughter is kidnapped by a ruthless gang. Agent Zero is let out of prison with the mission of going undercover to find the politician's daughter and return her safely. Using her deadly red handcuffs, she disposes of the criminals one by one.Unratedja6.70課の女 赤い手錠[{'iso_3166_1': 'JP', 'name': 'Japan'}]tt0283693[{'name': 'Toei Company', 'id': 9255}]1974-05-211974.0[{'cast_id': 4, 'character': 'Rei, Zero woman', 'credit_id': '52fe4690c3a368484e0978e1', 'gender': 0, 'id': 109691, 'name': 'Miki Sugimoto', 'order': 0, 'profile_path': '/5tTSr7DOCUinZ08jTW80srtwDDG.jpg'}, {'cast_id': 5, 'character': 'Yoshihide', 'credit_id': '52fe4690c3a368484e0978e5', 'gender': 2, 'id': 86247, 'name': 'Eiji Gô', 'order': 1, 'profile_path': None}, {'cast_id': 6, 'character': 'Nagumo', 'credit_id': '52fe4690c3a368484e0978e9', 'gender': 2, 'id': 10071, 'name': 'Tetsurō Tamba', 'order': 2, 'profile_path': '/lrWc0QjdAaP1VOWhwzczakdaEq.jpg'}, {'cast_id': 7, 'character': 'Kusaka', 'credit_id': '52fe4690c3a368484e0978ed', 'gender': 0, 'id': 120348, 'name': 'Hideo Murota', 'order': 3, 'profile_path': '/vDoABc3QM8MYOrxQLn0xfUl3vs5.jpg'}, {'cast_id': 8, 'character': 'Sesum, the Madame', 'credit_id': '52fe4690c3a368484e0978f1', 'gender': 1, 'id': 236092, 'name': 'Yôko Mihara', 'order': 4, 'profile_path': None}, {'cast_id': 9, 'character': 'Saburô Seki', 'credit_id': '52fe4690c3a368484e0978f5', 'gender': 0, 'id': 454568, 'name': 'Ichirô Araki', 'order': 5, 'profile_path': None}, {'cast_id': 10, 'character': 'Tooru Inaba', 'credit_id': '52fe4690c3a368484e0978f9', 'gender': 0, 'id': 234450, 'name': 'Seiji Endô', 'order': 6, 'profile_path': None}, {'cast_id': 11, 'character': 'Tani / Police', 'credit_id': '52fe4690c3a368484e0978fd', 'gender': 2, 'id': 138748, 'name': 'Rokkô Toura', 'order': 7, 'profile_path': None}][{'credit_id': '52fe4690c3a368484e0978d1', 'department': 'Directing', 'gender': 0, 'id': 109690, 'job': 'Director', 'name': 'Yukio Noda', 'profile_path': None}, {'credit_id': '52fe4690c3a368484e0978dd', 'department': 'Writing', 'gender': 2, 'id': 228553, 'job': 'Writer', 'name': 'Hirô Matsuda', 'profile_path': None}, {'credit_id': '52fe4690c3a368484e0978d7', 'department': 'Writing', 'gender': 0, 'id': 236091, 'job': 'Writer', 'name': 'Fumio Kônami', 'profile_path': None}]Yukio Noda/name/nm0633805/Miki Sugimoto, \nEiji Gô, \nTetsurô Tanba, \nHideo Murota/name/nm0999315/,/name/nm0323530/,/name/nm0848533/,/name/nm0605712/left_only
498611リットルの涙NaNNaNNaN[{'id': 18, 'name': 'Drama'}]98.015-year-old Ikeuchi Aya was an ordinary girl, the daughter of a family who works at a tofu shop, and a soon-to-be high schooler. However, odd things have been happening to Aya lately. She has been falling down often and walks strange. Her mother, Shioka, takes Aya to see the doctor. How will Aya react when told about her disease? And how will Aya live from now on?NaNja7.71リットルの涙[{'iso_3166_1': 'JP', 'name': 'Japan'}]tt0494724[{'name': 'TOEI', 'id': 7260}]2005-02-052005.0[{'cast_id': 3, 'character': '', 'credit_id': '52fe45bfc3a36847f80d82b5', 'gender': 1, 'id': 125941, 'name': 'Yoshimi Ashikawa', 'order': 0, 'profile_path': None}, {'cast_id': 4, 'character': 'Aya Kito', 'credit_id': '52fe45bfc3a36847f80d82b9', 'gender': 0, 'id': 125942, 'name': 'Asae Oonishi', 'order': 1, 'profile_path': None}, {'cast_id': 5, 'character': 'Hiroshi Mizuno', 'credit_id': '52fe45bfc3a36847f80d82bd', 'gender': 2, 'id': 125943, 'name': 'Naohito Fujiki', 'order': 2, 'profile_path': '/88ceM9xyfxdpJTNt6rLUhL9nl8Z.jpg'}, {'cast_id': 6, 'character': 'Keita Nakahara', 'credit_id': '52fe45bfc3a36847f80d82c1', 'gender': 0, 'id': 125944, 'name': 'Ryô Hashizume', 'order': 3, 'profile_path': '/ngr0HPBIiqTPPOrtYUqBKDQmE4h.jpg'}, {'cast_id': 7, 'character': 'Mizuo Ikeuchi', 'credit_id': '52fe45bfc3a36847f80d82c5', 'gender': 2, 'id': 97320, 'name': 'Takanori Jinnai', 'order': 4, 'profile_path': '/nfQUSM0VVQv79h8QXrtLXF5NcGd.jpg'}, {'cast_id': 8, 'character': 'Madoka Fujimura', 'credit_id': '52fe45bfc3a36847f80d82c9', 'gender': 0, 'id': 83926, 'name': 'Kazuko Katô', 'order': 5, 'profile_path': None}, {'cast_id': 9, 'character': 'Mari Sugiura', 'credit_id': '52fe45bfc3a36847f80d82cd', 'gender': 0, 'id': 125945, 'name': 'Saori Koide', 'order': 6, 'profile_path': '/3YjtMZiZx6cwsQ4ZJNgt3JRmWLr.jpg'}, {'cast_id': 10, 'character': '', 'credit_id': '52fe45bfc3a36847f80d82d1', 'gender': 0, 'id': 125946, 'name': 'Yoneko Matsukane', 'order': 7, 'profile_path': '/crhTojbcjXs9RxdgxbY49JQltuP.jpg'}, {'cast_id': 11, 'character': 'Saki Matsumura', 'credit_id': '52fe45bfc3a36847f80d82d5', 'gender': 0, 'id': 125947, 'name': 'Kana Matsumoto', 'order': 8, 'profile_path': None}, {'cast_id': 12, 'character': 'Yuji Kawamoto', 'credit_id': '52fe45bfc3a36847f80d82d9', 'gender': 2, 'id': 79037, 'name': 'Kenichi Matsuyama', 'order': 9, 'profile_path': '/8V1tX3womN0KOcX9FjYPLrGstKn.jpg'}, {'cast_id': 13, 'character': 'Rika Ikeuchi', 'credit_id': '52fe45bfc3a36847f80d82dd', 'gender': 0, 'id': 125948, 'name': 'Ani Miyoshi', 'order': 10, 'profile_path': None}, {'cast_id': 14, 'character': 'Kohei Onda', 'credit_id': '52fe45bfc3a36847f80d82e1', 'gender': 0, 'id': 125949, 'name': 'Momosuke Mizutani', 'order': 11, 'profile_path': None}, {'cast_id': 17, 'character': 'Hiroki Ikeuchi', 'credit_id': '52fe45bfc3a36847f80d82ed', 'gender': 0, 'id': 125951, 'name': 'Yuma Sanada', 'order': 14, 'profile_path': None}][{'credit_id': '52fe45bfc3a36847f80d82ab', 'department': 'Directing', 'gender': 0, 'id': 125939, 'job': 'Director', 'name': 'Riki Okamura', 'profile_path': None}, {'credit_id': '52fe45bfc3a36847f80d82b1', 'department': 'Writing', 'gender': 0, 'id': 125940, 'job': 'Writer', 'name': 'Aya Kito', 'profile_path': None}]NaNNaNNaNNaNleft_only
4986213号待避線より その護送車を狙えNaNNaNNaN[{'id': 28, 'name': 'Action'}, {'id': 80, 'name': 'Crime'}]79.0A sharpshooter kills two prisoners in a police van at night. The guard on the van is suspended for six months; he's Tamon, an upright, modest man. He begins his own investigation into the murders. Who were the victims, who are their relatives and girlfriends, who else was on the van that night? As he doggedly investigates, others die, coincidences occur, and several leads take him to the Hamaju Agency, which may be supplying call girls. Its owner is in jail, his daughter, the enigmatic Yuko, keeps turning up where Tamon goes. Tamon believes he can awaken good in people, but has he met his match? Will he solve the murders or be the next victim? And who is Akiba?Not Ratedja5.413号待避線より その護送車を狙え[{'iso_3166_1': 'JP', 'name': 'Japan'}]tt0054325[{'name': 'Nikkatsu', 'id': 955}]1960-01-271960.0[{'cast_id': 7, 'character': 'Daijiro Tamon', 'credit_id': '52fe4845c3a368484e0f07bd', 'gender': 0, 'id': 232078, 'name': 'Michitaro Mizushima', 'order': 0, 'profile_path': None}, {'cast_id': 8, 'character': 'Tsunako Ando', 'credit_id': '52fe4845c3a368484e0f07c1', 'gender': 0, 'id': 229404, 'name': 'Mari Shiraki', 'order': 1, 'profile_path': None}, {'cast_id': 9, 'character': 'Yuko Hamashima', 'credit_id': '52fe4845c3a368484e0f07c5', 'gender': 1, 'id': 111235, 'name': 'Misako Watanabe', 'order': 2, 'profile_path': None}, {'cast_id': 10, 'character': 'Jube Hamashima', 'credit_id': '52fe4845c3a368484e0f07c9', 'gender': 0, 'id': 72391, 'name': 'Shinsuke Ashida', 'order': 3, 'profile_path': None}, {'cast_id': 11, 'character': 'Goro Kashima', 'credit_id': '52fe4845c3a368484e0f07cd', 'gender': 0, 'id': 142454, 'name': 'Shoichi Ozawa', 'order': 4, 'profile_path': '/ht4yEUx9l4P9ckxu9zJPTjCDqvH.jpg'}, {'cast_id': 12, 'character': 'Kuji', 'credit_id': '52fe4845c3a368484e0f07d1', 'gender': 0, 'id': 226747, 'name': 'Ryôhei Uchida', 'order': 5, 'profile_path': None}, {'cast_id': 13, 'character': 'Akahori', 'credit_id': '52fe4845c3a368484e0f07d5', 'gender': 2, 'id': 83640, 'name': 'Tôru Abe', 'order': 6, 'profile_path': '/1pApwHFzrWUbX6BvoMRJ9E9edhc.jpg'}, {'cast_id': 14, 'character': 'Captain Takamura', 'credit_id': '52fe4845c3a368484e0f07d9', 'gender': 0, 'id': 1093067, 'name': 'Tatsuo Matsushita', 'order': 7, 'profile_path': None}, {'cast_id': 15, 'character': 'Ota', 'credit_id': '52fe4845c3a368484e0f07dd', 'gender': 0, 'id': 551804, 'name': 'Saburo Hiromatsu', 'order': 8, 'profile_path': None}, {'cast_id': 16, 'character': 'Osen', 'credit_id': '52fe4845c3a368484e0f07e1', 'gender': 0, 'id': 1093068, 'name': 'Reiko Arai', 'order': 9, 'profile_path': None}, {'cast_id': 17, 'character': 'Housewife', 'credit_id': '52fe4845c3a368484e0f07e5', 'gender': 0, 'id': 1093069, 'name': 'Toyo Fukuda', 'order': 10, 'profile_path': None}, {'cast_id': 18, 'character': "Merchant's wife", 'credit_id': '52fe4845c3a368484e0f07e9', 'gender': 0, 'id': 65512, 'name': 'Kotoe Hatsui', 'order': 11, 'profile_path': '/k3BhnRlm5HFHHxwpoEaL1e4SXgE.jpg'}, {'cast_id': 19, 'character': 'Masaki', 'credit_id': '52fe4845c3a368484e0f07ed', 'gender': 0, 'id': 1093070, 'name': 'Akira Hisamatsu', 'order': 12, 'profile_path': None}][{'credit_id': '52fe4845c3a368484e0f07f3', 'department': 'Writing', 'gender': 2, 'id': 18611, 'job': 'Screenplay', 'name': 'Shinichi Sekizawa', 'profile_path': None}, {'credit_id': '52fe4845c3a368484e0f07b9', 'department': 'Editing', 'gender': 2, 'id': 70632, 'job': 'Editor', 'name': 'Akira Suzuki', 'profile_path': None}, {'credit_id': '52fe4844c3a368484e0f07a1', 'department': 'Directing', 'gender': 2, 'id': 82461, 'job': 'Director', 'name': 'Seijun Suzuki', 'profile_path': '/fGZPvvEYaO1RLdW4PEcYp3IJcOW.jpg'}, {'credit_id': '52fe4845c3a368484e0f07b3', 'department': 'Crew', 'gender': 0, 'id': 554992, 'job': 'Cinematography', 'name': 'Shigeyoshi Mine', 'profile_path': None}, {'credit_id': '52fe4844c3a368484e0f07a7', 'department': 'Writing', 'gender': 0, 'id': 563708, 'job': 'Story', 'name': 'Kazuo Shimada', 'profile_path': None}, {'credit_id': '52fe4845c3a368484e0f07ad', 'department': 'Sound', 'gender': 0, 'id': 1093066, 'job': 'Original Music Composer', 'name': 'Koichi Kawabe', 'profile_path': None}]Seijun Suzuki/name/nm0840671/Michitarô Mizushima, \nMisako Watanabe, \nShôichi Ozawa, \nShinsuke Ashida/name/nm0594702/,/name/nm0913843/,/name/nm0654726/,/name/nm0038951/left_only
4986320世紀少年< 第1章> 終わりの始まり20000000.031244858.0NaN[{'id': 878, 'name': 'Science Fiction'}, {'id': 12, 'name': 'Adventure'}, {'id': 9648, 'name': 'Mystery'}]142.0In 1969, Kenji, an elementary school kid and his friends built a secret base during their summer holidays. They fantasized that they had to fight villains who were out to conquer the world and wrote them in the Book of Prophecies. Years later in 1997, Kenji becomes a convenience store manager and leads a regular life after giving up his dreams to become a rock star. His boring life is suddenly turned upside down when his old classmate dies mysteriously and an entire family in the neighbourhood disappears. At the same time, a religious cult and its mysterious leader, Friend emerges and a strange chain of events duplicating exactly the events described in the Book of Prophecies follow. Is this the beginning of the end of the world? Who is Friend?NaNja6.720世紀少年<第1章>終わりの始まり[{'iso_3166_1': 'JP', 'name': 'Japan'}, {'iso_3166_1': 'TH', 'name': 'Thailand'}]tt1155705[{'name': 'Cine Bazar', 'id': 5896}, {'name': 'Toho', 'id': 5897}]2008-08-192008.0[{'cast_id': 15, 'character': 'Kenji Endô', 'credit_id': '52fe46019251416c91045e63', 'gender': 2, 'id': 81350, 'name': 'Toshiaki Karasawa', 'order': 0, 'profile_path': '/gaoKYnp57eR7DJGviijCmnJKsV.jpg'}, {'cast_id': 17, 'character': 'Yukiji', 'credit_id': '52fe46019251416c91045e67', 'gender': 1, 'id': 81352, 'name': 'Takako Tokiwa', 'order': 2, 'profile_path': '/sfoeSQozRMSVKid0A8dWAd6ghXt.jpg'}, {'cast_id': 18, 'character': 'Yoshitsune', 'credit_id': '52fe46019251416c91045e6b', 'gender': 2, 'id': 46691, 'name': 'Teruyuki Kagawa', 'order': 3, 'profile_path': '/pCOQFnFHPDCtSuYmMT3Q24Yf0SE.jpg'}, {'cast_id': 19, 'character': 'Maruo', 'credit_id': '52fe46019251416c91045e6f', 'gender': 0, 'id': 131106, 'name': 'Hidehiko Ishizuka', 'order': 4, 'profile_path': None}, {'cast_id': 20, 'character': 'Mon-chan', 'credit_id': '52fe46019251416c91045e73', 'gender': 0, 'id': 131107, 'name': 'Takashi Ukaji', 'order': 5, 'profile_path': '/wawHy4VSbKvhl6JOhIXUCGbTMwi.jpg'}, {'cast_id': 21, 'character': 'Keroyon', 'credit_id': '52fe46019251416c91045e77', 'gender': 2, 'id': 70211, 'name': 'Hiroyuki Miyasako', 'order': 6, 'profile_path': '/t5HCiv3wEnWLn3SqcZlmLOJKXki.jpg'}, {'cast_id': 22, 'character': 'Donkey', 'credit_id': '52fe46019251416c91045e7b', 'gender': 2, 'id': 91873, 'name': 'Katsuhisa Namase', 'order': 7, 'profile_path': '/ppbpmLydzWXzuVWbBr46VP3PZeT.jpg'}, {'cast_id': 23, 'character': 'Yamane', 'credit_id': '52fe46019251416c91045e7f', 'gender': 2, 'id': 58449, 'name': 'Fumiyo Kohinata', 'order': 8, 'profile_path': '/nUugr8JH1cmGRPcMFvSQibShojz.jpg'}, {'cast_id': 24, 'character': 'Fukubê', 'credit_id': '52fe46019251416c91045e83', 'gender': 0, 'id': 121716, 'name': 'Kuranosuke Sasaki', 'order': 9, 'profile_path': '/AfqaeQ4yrA3kQsDDoREN9bcUeKb.jpg'}, {'cast_id': 25, 'character': 'Yan-bô', 'credit_id': '52fe46019251416c91045e87', 'gender': 2, 'id': 73139, 'name': 'Shirô Sano', 'order': 10, 'profile_path': '/dOqwnvpAvLtfEV0YJTuJbnCeNUP.jpg'}, {'cast_id': 26, 'character': 'Mâ-bô', 'credit_id': '52fe46019251416c91045e8b', 'gender': 2, 'id': 73139, 'name': 'Shirô Sano', 'order': 11, 'profile_path': '/dOqwnvpAvLtfEV0YJTuJbnCeNUP.jpg'}, {'cast_id': 28, 'character': 'Moroboshi', 'credit_id': '52fe46019251416c91045e93', 'gender': 0, 'id': 13256, 'name': 'Kanji Tsuda', 'order': 13, 'profile_path': '/5SF6Pb7axi78uknOGWB0eF4Beam.jpg'}, {'cast_id': 29, 'character': 'Friendship Party Promotor', 'credit_id': '52fe46019251416c91045e97', 'gender': 2, 'id': 90159, 'name': 'Takashi Fujii', 'order': 14, 'profile_path': '/9HLZxTouPqyzyQhw6rwui4gdQzg.jpg'}, {'cast_id': 30, 'character': 'Friendship Party Promotor', 'credit_id': '52fe46019251416c91045e9b', 'gender': 0, 'id': 131108, 'name': 'Hanako Yamada', 'order': 15, 'profile_path': None}, {'cast_id': 31, 'character': 'Otcho', 'credit_id': '52fe46019251416c91045e9f', 'gender': 2, 'id': 80754, 'name': 'Etsushi Toyokawa', 'order': 16, 'profile_path': '/gkmAbZ47w7jFfr1ASgs1cqZY9Mi.jpg'}, {'cast_id': 33, 'character': 'Miki Shikishima', 'credit_id': '569aa5cfc3a36872b7000aef', 'gender': 1, 'id': 1044524, 'name': 'Nana Katase', 'order': 17, 'profile_path': '/1QtpvcxyEH5VXaqhfWj3B6FaY3a.jpg'}, {'cast_id': 34, 'character': 'Kiriko Endo', 'credit_id': '56a9bb019251415475001edd', 'gender': 1, 'id': 71641, 'name': 'Hitomi Kuroki', 'order': 18, 'profile_path': '/jk0sqAm1f4jvt8IjP1dfuPxbGDC.jpg'}, {'cast_id': 36, 'character': 'Comic Artist', 'credit_id': '592a4e249251413b50056734', 'gender': 2, 'id': 87657, 'name': 'Mirai Moriyama', 'order': 19, 'profile_path': None}][{'credit_id': '57fc644ec3a368440f004c0d', 'department': 'Production', 'gender': 2, 'id': 78343, 'job': 'Executive Producer', 'name': 'Seiji Okuda', 'profile_path': '/cE9kgHpTWChph8zNZ8N5hSGfweN.jpg'}, {'credit_id': '54c26c3cc3a36878fb003745', 'department': 'Directing', 'gender': 2, 'id': 81355, 'job': 'Director', 'name': 'Yukihiko Tsutsumi', 'profile_path': '/2ebJXJE46FNiKrQ8nSJOBPV6YAT.jpg'}, {'credit_id': '52fe46019251416c91045e17', 'department': 'Writing', 'gender': 0, 'id': 115934, 'job': 'Screenplay', 'name': 'Yasushi Fukuda', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e1d', 'department': 'Writing', 'gender': 0, 'id': 115935, 'job': 'Screenplay', 'name': 'Takashi Nagasaki', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e23', 'department': 'Writing', 'gender': 0, 'id': 115936, 'job': 'Screenplay', 'name': 'Naoki Urasawa', 'profile_path': '/xpXOtyO3gvJZbnRfwhXDBeeVwEP.jpg'}, {'credit_id': '52fe46019251416c91045e29', 'department': 'Writing', 'gender': 0, 'id': 115937, 'job': 'Screenplay', 'name': 'Yusuke Watanabe', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e2f', 'department': 'Production', 'gender': 0, 'id': 115938, 'job': 'Producer', 'name': 'Morio Amagi', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e35', 'department': 'Production', 'gender': 0, 'id': 115939, 'job': 'Producer', 'name': 'Ryûji Ichiyama', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e3b', 'department': 'Production', 'gender': 0, 'id': 115940, 'job': 'Producer', 'name': 'Nobuyuki Iinuma', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e41', 'department': 'Production', 'gender': 0, 'id': 115941, 'job': 'Producer', 'name': 'Kiyoshi Inoue', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e47', 'department': 'Production', 'gender': 0, 'id': 115942, 'job': 'Producer', 'name': 'Futoshi Ohira', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e53', 'department': 'Sound', 'gender': 0, 'id': 115944, 'job': 'Music', 'name': 'Ryomei Shirai', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e59', 'department': 'Camera', 'gender': 0, 'id': 115945, 'job': 'Director of Photography', 'name': 'Satoru Karasawa', 'profile_path': None}, {'credit_id': '52fe46019251416c91045e5f', 'department': 'Editing', 'gender': 0, 'id': 115946, 'job': 'Editor', 'name': 'Nobuyuki Ito', 'profile_path': None}]Yukihiko Tsutsumi/name/nm1066739/Toshiaki Karasawa, \nEtsushi Toyokawa, \nTakako Tokiwa, \nTeruyuki Kagawa/name/nm0438960/,/name/nm0870317/,/name/nm0865764/,/name/nm0434596/left_only
498643-4X10月NaNNaN1471.0[{'id': 80, 'name': 'Crime'}, {'id': 28, 'name': 'Action'}, {'id': 35, 'name': 'Comedy'}]96.0Masaki, a baseball player and gas-station attendant, gets into trouble with the local Yakuza and goes to Okinawa to get a gun to defend himself. There he meets Uehara, a tough gangster, who is in serious debt to the yakuza and planning revenge.Not Ratedja6.63-4x10月[{'iso_3166_1': 'JP', 'name': 'Japan'}]tt0098967[{'name': 'Bandai Visual Company', 'id': 528}, {'name': 'Shochiku-Fuji Company', 'id': 6919}]1990-09-151990.0[{'cast_id': 2, 'character': 'Masaki', 'credit_id': '52fe4522c3a368484e04a661', 'gender': 2, 'id': 20311, 'name': 'Yūrei Yanagi', 'order': 0, 'profile_path': '/nLCalu0xfwfv6BFquyrTQ8Ad1Kj.jpg'}, {'cast_id': 3, 'character': 'Sayaka', 'credit_id': '52fe4522c3a368484e04a665', 'gender': 1, 'id': 20330, 'name': 'Yuriko Ishida', 'order': 1, 'profile_path': '/ntaS0ZnY7Z3523R1ov6BsrjdVyc.jpg'}, {'cast_id': 4, 'character': 'Takashi Iguchi', 'credit_id': '52fe4522c3a368484e04a669', 'gender': 0, 'id': 13277, 'name': 'Gadarukanaru Taka', 'order': 2, 'profile_path': None}, {'cast_id': 5, 'character': 'Kazuo', 'credit_id': '52fe4522c3a368484e04a66d', 'gender': 0, 'id': 20344, 'name': 'Dankan', 'order': 3, 'profile_path': None}, {'cast_id': 6, 'character': 'Akira', 'credit_id': '52fe4522c3a368484e04a671', 'gender': 0, 'id': 20341, 'name': 'Makoto Ashikawa', 'order': 4, 'profile_path': '/lOcFd8QSCXMHP727uPRQX2oFZ2b.jpg'}, {'cast_id': 7, 'character': 'Otomo, the gang boss', 'credit_id': '52fe4522c3a368484e04a675', 'gender': 2, 'id': 96637, 'name': 'Hisashi Igawa', 'order': 5, 'profile_path': '/ua7RA1qr9LD9k0vOGgNwwhYhkIr.jpg'}, {'cast_id': 8, 'character': 'Minamizaka', 'credit_id': '52fe4522c3a368484e04a679', 'gender': 0, 'id': 96638, 'name': 'Johnny Okura', 'order': 6, 'profile_path': '/8PgP7xp8FqHB57LqTYtmgX9BawO.jpg'}, {'cast_id': 9, 'character': 'Tamagi', 'credit_id': '52fe4522c3a368484e04a67d', 'gender': 0, 'id': 96639, 'name': 'Katsuo Tokashiki', 'order': 7, 'profile_path': None}, {'cast_id': 10, 'character': 'Uehara', 'credit_id': '52fe4522c3a368484e04a681', 'gender': 2, 'id': 3317, 'name': 'Takeshi Kitano', 'order': 8, 'profile_path': '/xkXZ7HC8uyhu5MtNnbdP8mhdiFN.jpg'}, {'cast_id': 11, 'character': 'Kanai', 'credit_id': '52fe4522c3a368484e04a685', 'gender': 0, 'id': 68989, 'name': 'Hitoshi Ozawa', 'order': 9, 'profile_path': None}][{'credit_id': '52fe4522c3a368484e04a68b', 'department': 'Writing', 'gender': 2, 'id': 3317, 'job': 'Screenplay', 'name': 'Takeshi Kitano', 'profile_path': '/xkXZ7HC8uyhu5MtNnbdP8mhdiFN.jpg'}, {'credit_id': '52fe4522c3a368484e04a65d', 'department': 'Directing', 'gender': 2, 'id': 3317, 'job': 'Director', 'name': 'Takeshi Kitano', 'profile_path': '/xkXZ7HC8uyhu5MtNnbdP8mhdiFN.jpg'}, {'credit_id': '52fe4522c3a368484e04a69d', 'department': 'Crew', 'gender': 0, 'id': 4998, 'job': 'Cinematography', 'name': 'Katsumi Yanagijima', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a691', 'department': 'Production', 'gender': 0, 'id': 31077, 'job': 'Producer', 'name': 'Toshio Nabeshima', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a697', 'department': 'Production', 'gender': 0, 'id': 73143, 'job': 'Executive Producer', 'name': 'Kazuyoshi Okuyama', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a6b5', 'department': 'Crew', 'gender': 0, 'id': 146656, 'job': 'Stand In', 'name': 'Makoto Tsugawa', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a6af', 'department': 'Lighting', 'gender': 0, 'id': 146663, 'job': 'Gaffer', 'name': 'Hitoshi Takaya', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a6a3', 'department': 'Editing', 'gender': 0, 'id': 554163, 'job': 'Editor', 'name': 'Toshio Taniguchi', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a6a9', 'department': 'Crew', 'gender': 0, 'id': 1108681, 'job': 'Special Effects', 'name': 'Kikuo Notomi', 'profile_path': None}, {'credit_id': '52fe4522c3a368484e04a6bb', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1108686, 'job': 'Makeup Artist', 'name': 'Yoshie Hamada', 'profile_path': None}]Takeshi Kitano/name/nm0001429/Takeshi Kitano, \nYûrei Yanagi, \nYuriko Ishida, \nTaka Guadalcanal/name/nm0001429/,/name/nm0648764/,/name/nm0410942/,/name/nm0407276/left_only
49865SMガールズ セイバーマリオネットRNaNNaNNaN[{'id': 878, 'name': 'Science Fiction'}, {'id': 35, 'name': 'Comedy'}]30.0Jr., the heir of Romana and his battle sabers Cherry and Lime, who have girl circuits are enjoying their peaceful life in Romana. Suddenly, the evil Star-Face and his sexadolls attack Romana in order to take over so Star-Face can become the next High Official. In order to truly become the next High Official and ruler of Romana, he must first eliminate Jr. This begins a battle for, not only Jr.'s life, but for all of Romana.NaNja0.0smガールズセイバーマリオネットr[{'iso_3166_1': 'JP', 'name': 'Japan'}]tt3422000[{'name': 'ANIMATE', 'id': 13064}, {'name': 'Zero G Room', 'id': 53165}]1995-05-211995.0[{'cast_id': 21, 'character': 'Cherry (voice)', 'credit_id': '57800ae3c3a3682e16001b45', 'gender': 1, 'id': 555550, 'name': 'Yuri Shiratori', 'order': 0, 'profile_path': '/obLK8jNQo7McZ9CRvmcEk1SS5DQ.jpg'}, {'cast_id': 1, 'character': 'Lime', 'credit_id': '55504011c3a36852210019cf', 'gender': 1, 'id': 40325, 'name': 'Megumi Hayashibara', 'order': 1, 'profile_path': '/aYeXdOAsEh2xItISYDefJfvcdA.jpg'}, {'cast_id': 0, 'character': 'Bloodberry', 'credit_id': '55503fff92514105cf000799', 'gender': 1, 'id': 143502, 'name': 'Akiko Hiramatsu', 'order': 2, 'profile_path': '/v0F4QOBvuwoAbQKxdBVBBai4hEb.jpg'}, {'cast_id': 2, 'character': 'Junior', 'credit_id': '555040209251411b8b000619', 'gender': 0, 'id': 1221862, 'name': 'Yuka Imai', 'order': 3, 'profile_path': '/rs2zugdmDqNvqmeitF4oWzSayoC.jpg'}, {'cast_id': 4, 'character': 'Face', 'credit_id': '55504041c3a3682254005e31', 'gender': 0, 'id': 122726, 'name': 'Hikaru Midorikawa', 'order': 4, 'profile_path': '/67LlDJlFDeIrZlI4o6IM23fG7Tr.jpg'}, {'cast_id': 5, 'character': 'Brid', 'credit_id': '555040519251416a93000a07', 'gender': 0, 'id': 101913, 'name': 'Kikuko Inoue', 'order': 5, 'profile_path': '/5L2IMLJIN7XynIn5tVr80dcaKNG.jpg'}, {'cast_id': 6, 'character': 'Edge', 'credit_id': '555040629251413eee000cd8', 'gender': 1, 'id': 555104, 'name': 'Urara Takano', 'order': 6, 'profile_path': '/8t7c1CCvjK9f3TMvoZKNM4mIQpq.jpg'}, {'cast_id': 7, 'character': 'Kyanny', 'credit_id': '55504072c3a36818700068cc', 'gender': 0, 'id': 112138, 'name': 'Yuko Mizutani', 'order': 7, 'profile_path': '/n7zoT3w5pkorKvIBrFBhqjAamW2.jpg'}, {'cast_id': 8, 'character': 'Villey', 'credit_id': '55504081c3a3687953003b88', 'gender': 0, 'id': 229191, 'name': 'Banjou Ginga', 'order': 8, 'profile_path': None}, {'cast_id': 10, 'character': 'sexadoll A(Act 2)', 'credit_id': '555040a4c3a36818700068d8', 'gender': 1, 'id': 1241505, 'name': 'Kujira', 'order': 10, 'profile_path': '/akW1HjQxB8qrceyok2WzxOEIpyn.jpg'}, {'cast_id': 11, 'character': 'Patrol Saber(Act 1)', 'credit_id': '555040b4c3a3687f63003c10', 'gender': 1, 'id': 554874, 'name': 'Miyuki Matsushita', 'order': 11, 'profile_path': None}, {'cast_id': 12, 'character': 'Sunchest(Act 1)', 'credit_id': '555040c4c3a3687f63003c16', 'gender': 2, 'id': 62318, 'name': "Shin'ichirou Miki", 'order': 12, 'profile_path': '/7eU3YGGkQsTuiQU3xvSRA6nNZdC.jpg'}, {'cast_id': 13, 'character': 'sex doll B(Act 2)', 'credit_id': '555040d5c3a3686d06001071', 'gender': 1, 'id': 553925, 'name': 'Tomoko Nakajima', 'order': 13, 'profile_path': '/nO2YfEcGEuNoFW6wpF4N1gUgc1v.jpg'}, {'cast_id': 14, 'character': 'Announcer(Act 1)', 'credit_id': '555040e6c3a3687f63003c1d', 'gender': 2, 'id': 112278, 'name': 'Wataru Takagi', 'order': 14, 'profile_path': '/j0mQoLTEwzWYoJTsPtl9X25V43R.jpg'}, {'cast_id': 15, 'character': 'Bureaucrat(Act 2)', 'credit_id': '555040f7c3a3685bbf000c30', 'gender': 2, 'id': 553282, 'name': 'Yasunori Matsumoto', 'order': 15, 'profile_path': '/wStHWJbhaoN1uNIPmRUH32ry6co.jpg'}, {'cast_id': 16, 'character': 'Adali(Act 2) Lun(Act 1)', 'credit_id': '555041079251411b8b000642', 'gender': 1, 'id': 83768, 'name': 'Yuko Miyamura', 'order': 16, 'profile_path': '/aXRz2l0YzDtiuJSTkKJPaeKdLNN.jpg'}, {'cast_id': 22, 'character': 'Old Guy (voice)', 'credit_id': '58070d4292514170b7018529', 'gender': 2, 'id': 142704, 'name': 'Kenichi Ogata', 'order': 17, 'profile_path': '/wkwtFf4yGpErhxt5ira7nEi0trt.jpg'}][{'credit_id': '55504222c3a3685bbf000c5c', 'department': 'Writing', 'gender': 2, 'id': 77923, 'job': 'Storyboard', 'name': 'Kazuya Tsurumaki', 'profile_path': '/5yc1Q20SafIjC9gxjz1OhDQbzye.jpg'}, {'credit_id': '555041229251416a93000a28', 'department': 'Directing', 'gender': 0, 'id': 1249974, 'job': 'Director', 'name': 'Koji Masunari', 'profile_path': None}, {'credit_id': '5550413c9251413eee000cf2', 'department': 'Crew', 'gender': 0, 'id': 1249974, 'job': 'Script', 'name': 'Koji Masunari', 'profile_path': None}, {'credit_id': '5550423c9251413f32000da6', 'department': 'Sound', 'gender': 0, 'id': 1451781, 'job': 'Music', 'name': 'Toshiyuki Omori', 'profile_path': None}]NaNNaNNaNNaNleft_only